1 Introduction

An exhaustive attempt to curtail the load demand in a home during high consumption intervals by altering energy consumption from the crest load period to other intervals of the day is termed Home Energy Management (HEM). Earlier it was considered to be the responsibility of the utility company to perform Demand Side Management (DSM) by introducing various incentive schemes and introducing energy-efficient products on the energy market. In an attempt to increase user comfort at home, more electric loads are being installed. The consequence is that the connected load in a home is getting increased and thereby it indirectly increases the peak load demand as well as the electricity bill. A typical example is demonstrated in Fig. 1, which shows the bimonthly (every 2 months) power consumption in Kilowatt-hours of a home located in the state of Kerala, India for 7 years and Fig. 2, shows energy consumption difference over 6 years.

Fig. 1
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Bimonthly energy consumption

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Fig. 2
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Increased energy consumption pattern over 6 years

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Figure 1, shows that the average load consumption of this home is only increasing and therefore their energy cost is also inclining. If this pattern of load consumption occurs in other houses also then it will result in an increased carbon footprint contribution by the utility company. Hence the consumer should understand the fact that the demand side has to be managed in cooperation with the utility company, for a greener world. Thus, the foundation for an energy management attempt at the consumer side is nothing but making consumers aware of the importance of saving energy. Table 1, shows a sample from an energy audit survey conducted on more than 200 Low Tension (LT) domestic consumers in the state of Kerala, India to analyze the energy consumption pattern of their homes.

Table 1 A sample of the energy audit survey conducted on LT domestic consumers
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A simple reengineering of conventional lighting loads with low energy-consuming units, utilizing electronic regulators for speed control of the fan, or migrating to an energy star rated appliance will reduce the bill amount to a certain extent as shown in Table 1. The additional expense incurred for this migration can be met in a couple of months due to reduced energy consumption. Around 390 Kilowatt-hour of energy can be saved in a month and thus 4.68 Megawatt-hour in a year by the sample of consumers in Table 1. As the next phase of the migration attempt, a careful choice of DSM technique will further reduce the power consumption and peak load curve of a home. An exhaustive lineup of the DSM strategies was presented by reconsidering the DSM theoretical framework and revealed that the carbon footprint of an energy generation plant can be reduced by a DSM effort and it also increases the resilience in supply system operation [1]. The focus of any Demand Response (DR) program should not be limited to the user’s comfort level boosting but also to raising the revenue of utility companies [2]. The major challenges faced by a DSM are the unavailability of a smart metering facility, lack of competitiveness of DSM with the existing demand-side approach, system complexity, and poor market incentives [3]. It even gets worse due to the poor user knowledge of the benefits of a DSM. An interesting area of research is where the electricity smart meter together with the water and gas meter can be linked to a bigger network having the potential of energy saving [4]. Since any demand response scheme involves handling bulk real-time data and execution of complex tasks, Artificial Intelligence and Machine Learning techniques can be extensively utilized for enabling DSM [5]. In Sect. 2, the factors influencing energy management at home are investigated along with the role of smart devices, electric vehicles as storage as well as supply and renewable resource utilization. In Sect. 3, the need to adopt a HEM system is explained with a major focus on investigating the type of optimization strategies for load scheduling and different pricing methods. The flexibility of a cloud-based HEM system is also covered. In Sect. 4, the socio-economic relevance of implementing a HEM strategy is explained. Section 5, concludes the investigation attempt.

2 Factors influencing energy management at home

2.1 Lifestyle survey

A survey on the current lifestyle helps to understand the distribution of load and the possibility of storage at a home. Scheduling surveys help to understand the mindset of consumers on complying with a demand response program based on incentives [6]. This helps to develop new policies for formulating DR programs. Earlier the demand response offered by the utility company focused on load reduction only but now the demand response means not only load reduction or schedule, but also the power generation and storage from the user end and supply into the grid [7]. This calls for a wider categorization of users from the conventional classification like home, small-scale, and large-scale users. The users may be also classified as, users with individual plug-in vehicles and a fleet of plug-in vehicles [8]. A survey on a residential load should be systematically conducted considering factors like types of load, user choice, occupancy, and climate. The questionnaire prepared should cover areas like the willingness to load shifting, revealing monthly bill amount, the disclosure of appliance details, the day, and the number of times of usage of certain appliances like washing machines [9].

While designing a home energy management system, a higher priority may be given to user comfort. To increase user comfort, the lighting devices may be operated in three modes and they are ON, OFF, and DIM mode [10]. An interspersed solution that reengineers the electricity demand by prediction and thereby lowers peak load in a region for a period can be recommended that considers user comfort also. This is achieved by developing a decision-backing system that can forecast electricity requirements in residential buildings. Based on the user’s lifestyle with other social factors, the run time of the appliances can be scheduled with the help of a predictor [11]. Comparative analysis of similar loads is another way of surveying to find out factors influencing user comfort. Methods like subjecting two similar buildings under the same climatic condition to different temperature settings without compromising the comfort level and thereby surveying the efficient management of air conditioners as a load is also possible [12]. Also, the control of circulating air quality is an essential requirement that helps to improve user comfort [13].

A meticulous way of load recognition and its monitoring is necessary while handling intrusive and non-intrusive load monitoring frameworks for appliance management [14]. The significance of a non-intrusive approach to load monitoring was presented in Hosseini et al. [15] which also proposed an advanced non-intrusive load monitoring method that could realize real-time structure as an imminent side of the forthcoming energy network. The different techniques used to determine the power flexibility of a building were demonstrated in Pallonetto et al. [16]. A pilot survey on the existing load conditions at a home helps in framing an appropriate HEM system. It is also observed that unexpected events like lockdown due to the Covid-19 pandemic also change the power consumption patterns in a residential areas [17,18,19].

2.2 Role of smart devices in energy saving

Smart home appliances are a set of intelligent devices that can give information on consumer's requirements, facilitating a better comfort level without the burden of complex methodologies and an intuitive interface to the user [20]. The networking system utilizing IEEE 802.15.4 and ZigBee will help to reduce the wiring cost [10] and thus help in decreasing the total cost of implementing a smart HEMS. A cluster of algorithms can be used to realize new energy efficiency services like itemized energy bills with the help of smart devices like smart meters and smartphones [21]. The appliance signature collection is possible with the help of an application that runs on a smartphone. This process has another advantage that in the future an appliance signature database can be prepared and may be uploaded to a community platform for better load reduction.

User authentication and voice activation are other features that can be included while designing a smart living system using smart devices [22]. A low-cost smart home system with an Android interface was presented that also incorporated home security and other alert systems. Future smart devices like the smart bed, smart refrigerator, etc. were depicted in Park et al. [20], out of which some were working prototype implementations while some were onscreen virtual prototype models. Using this energy-efficient technology may also create a condition called the Rebound Effect where the demand increases as a result of the latest technology adoption and load shifting [23].

2.3 Influence of alternate energies in HEM and merit of forecasting

Concerning the annual report 2019–20 of the Ministry for New and Renewable Energy, [24], Fig. 3, shows the source-wise installed power generation capacity in Megawatt and its percentage contribution to the total power generation capacity of the nation as of 31.12.2019 in India.

Fig. 3
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Installed power generation capacity in India (in MW) [24]

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The nation's Intended Nationally Determined Contribution (INDC) was built on its goal of installing 175 gigawatts of renewable power capacity by the year 2022 [24] and achieved 167.75 gigawatts as per the latest 2022–23 annual report [25]. With the implementation of a smart grid along with the microgrid operation using renewable energy resources, it is possible to reduce the carbon footprint of thermal power plant companies [26, 27]. Deploying renewable energies like solar energy and wind energy on the demand side, power saving can be achieved thereby reducing the energy cost also [28, 29]. For instance, by utilizing the potential of renewable energy, the average power consumption of a 198-m square testbed was reduced by 7.3%, when operated for a 1-month time [30].

A decentralized, time-based demand side administration method that helped to organize the appliance load to pursue a preplanned day-ahead energy production by the microgrid that works on precalculated user aggregate load was proposed [31]. An additional penalty was imposed on those consumers who deviated from their predicted demand during actual consumption. The administration of demand-side flexibility helps in better integration of intermittent alternate energy sources into home grids [32]. With facilities like power line communication (PLC), monitoring, recording, and forecasting are possible now. The smart HEMS demonstrated in Han et al. [33] considered both generation and consumption for lowering usage costs. A PLC-based data transfer scheme was used to monitor solar panels for maintenance work.

PV is a widely acknowledged renewable energy source [34], for promoting the mass-level installation of Photo Voltaic (PV) cells, methods that will accurately forecast the PV power output are essential. This will improve the system reliability also [35]. To prove it, a PV energy output prediction model for an individual station was formulated with a day-ahead pricing scheme. The model utilized weather forecasting data along with the historical data and the principle of support vector machine. Combining stochastic and deterministic forecasting methods to form hybrid methods, may yield a better result in the weather forecasting approach [36]. Variations in solar output in a region due to factors like atmospheric aerosol level, presence of other gases, and cloud cover variability have to be taken into account while forecasting solar power output. While choosing solar power as a renewable resource, there are three main ways of network integration and they are classified as on-grid, off-grid, and Hybrid solar systems [37]. Table 2, illustrates the comparative analysis.

Table 2 Methods of utilizing solar power for HEM
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In a developing country like India, after wind and solar energy, the major renewable energy contributor is biopower. This is clear from Fig. 4, which reveals the renewable energy cumulative achievement in Megawatts [24]. Biopower can be utilized exhaustively during nighttime as an alternative to solar energy and in places where the wind is intermittent. Thus, implementing hybrid renewable energy systems [38] that utilize energy from multiple energy sources helps to meet all-time energy demands in a home. It is also observed that many countries are making policies that restrict buildings to limit the on-site energy consumption under on-site renewable generation value [39].

Fig. 4
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Renewable energy cumulative achievement (in MW) [24]

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Harvesting the possibilities of renewable energy should not be limited to the production of electricity alone in a home. This form of energy can be utilized for other purposes also which will indirectly help in reducing the power consumption of a home as well as saving money through other channels. Figure 5, shows the added uses, and benefits of utilizing renewable energy in a home apart from producing electricity alone.

Fig.5
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Renewable energy technologies (RET) added uses and benefits

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2.4 Electric vehicle as storage and supply

With the invention of high-capacity batteries with a reduced charging time, the world has started to witness an era of Electric vehicles (EVs) on roads. Most of the IC engine-based vehicle manufacturing companies are in the phase of starting a new R&D unit for introducing electric vehicles into the market [40]. Electric vehicle helps in diverting the peak demand, thereby reducing the capital cost of expansion for a utility company due to electric vehicle's energy storage provision [41, 42] and because of this, the saved money can be utilized to generate more renewable energy. If schemes like financial incentives are provided by the utility company, then more customer participation can be expected for DSM. Also, if the government introduces incentive schemes, it may motivate people to purchase an electric vehicle rather than buying a vehicle with an IC engine. The vehicle-to-grid (V2G), system helps to attain a better demand response by utilizing an electric vehicle power storage facility. The battery aging and its degradation factor should also be accounted for while using an energy storage system [43]. When using an electric vehicle as storage and supply, utmost care is required while plugging into the distribution system otherwise, it may degrade the power quality of the system [44].

Optimizing the charging cycle is the major concern when dealing with V2G integration. Optimizing the charging cycle of an EV utilizing DSM by examining the load profile is an option [41] for this. The EV connectivity impacts on the smart grid were discussed in Mahmud et al. [45] along with an analysis of various EV technology and standards for reliable and economic operation. Many working-class private vehicle owners park their vehicles at office premises during the daytime. Facilities like fleet charging help to utilize renewable energy during the day to charge the vehicle. A nominal fee may be collected from the employees who are utilizing this facility and in return their peak time charging of EVs at home will be reduced and the same storage facility of EVs may be utilized as a power source for some home appliances to level the load curve.

3 Why a home energy management system (HEMS)?

HEMS is defined as an organized system that evaluates, scrutinizes, and administers an available cluster of energy appliances in a home for optimally scheduling it so that the target goal is achieved [44, 46]. In other words, a HEM system provides information on home energy consumption data concerning real-time and prepares the appliance schedule chart focusing on power consumption optimization at home [47]. Thus, any HEMS should have a simple but realistic algorithm behind it that has the advantage of significant bill reduction and maintaining user satisfaction [48]. Another objective of any HEMS should also be to increase user privacy and that means limiting the sharing of details on the amount of energy usage [49]. The time taken to exchange data between different HEM components and load managers is crucial while designing a HEMS system. A hardware demonstration of a ZigBee-based HEM was proposed in paper [50] which analyzed on average transmission and reception delay of a load controller with the HEM unit. The proposed system also analyzed the performance of each load when it is being managed by the HEM. Another building block of any HEMS will be a home server that controls power use by monitoring the load consumption pattern to the energy pricing scheme and also scheduling the home appliances accordingly.

While designing a HEM system in India, care should be taken to include most of the appliances in a conventional house otherwise replacing the appliances with their equivalent smart one would incur an additional burden on the consumer that makes the demand response scheme less attractive. A home energy management algorithm that observes and appoints the appliances in any conventional house for downsizing the consumption cost by reducing the power consumption was proposed in Shakeri et al. [51]. The main motto was not to compromise on human comfort. An intelligent and automatic home network may also be implemented using smart nodes which have detecting, handling, and networking ability [10]. There are 7 levels of smartness with smart home technologies starting from basic (level 0) to a level of aggregation (level 6) that can interconnect neighborhoods or even states [52] shown in Table 3. The absence of any smart technology corresponds to level 0, the presence of segregated smart technology corresponds to level 1, automation possibility corresponds to level 2, automated and anticipatory technology availability corresponds to level 3, a technology that can learn, alter, and adapt corresponds to level 4, automated technology that can meet all predicted requirement corresponds to level 5 and smart technology that interconnects the neighborhood corresponds to level 6.

Table 3 Levels of smartness with smart home technologies [52]
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Care should be taken while designing a HEM system so that along with reducing energy usage at home it should also help the utility company to curtail peak time loading and skillfully balance system power distribution which decreases greenhouse gas emissions [49]. In this context, an appliance monitoring and scheduling method was proposed where each user has to be only aware of the cost of electricity and will substantially favor the reduction of generation cost, crest load, and load fluctuation [53]. Another method was to propose a HEM system that will also help the power utility providers to avert distribution transformer overload due to future loads like electric vehicles [50]. The energy providers may have to tackle the new peak demand once the day ahead timely prices are implemented [54]. Therefore, effective steps have to be taken while designing a DSM method that relieves the burden on the utility sector. The positives of adopting a DSM method in connection with power generation, transmission, and distribution were showcased in Strbac [3]. Better management of the demand and supply can be achieved by adopting DSM in places where alternate energy availability is intermittent.

A properly communicated coordination among smart homes trims peak load and cost with limited violation of user comfort, otherwise, their interaction may cause network instability and rebound peak [55]. Thus, a community-based microgeneration and selling facility for home energy users will help to subside the peak load demand and it also helps generate revenue for home users which indirectly balances their consumption cost [56, 57]. A basic understanding of the pricing scheme, load optimization techniques, and types of efficient communication schemes is also required while designing a smart HEM system.

3.1 Types of pricing technique

Due to the addition of alternate power sources into the power grid and the integration of the latest technology in HEM systems, the conventional tariff system became incompetent in assuring fair billing to consumers [58]. The pricing scheme for a consumer should be carefully formulated so that it should promote optimum usage of electric power in different sectors like home, SSI, and LSI. The demographic, behavioral, and geographical market segmentation should be also evaluated. An intensive review of dynamic pricing that stimulates demand response was stated in Dutta and Mitra [59]. The aim was to flatter the load curve and thereby reduce the carbon footprint for a greener world. One among the different types of pricing schemes used for home energy optimization is Real-Time Pricing (RTP) with dynamic behavior where price changes at regular intervals of the day (per hour or for a few minutes). This helps the consumers in appropriate load scheduling so that, their average energy cost is reduced. This method also helps in leveling the load curve. The main problem while dealing with this pricing technique is that there is a chance of formation of off-peak load in a day as when most of the consumers attempt for a load rescheduling.

Curtailing the peak demand can also be done by adopting another pricing method known as Critical Peak Pricing (CPP). Here cost during peak hours remains the same for all days and therefore the user will be consonantly motivated to schedule their load on other periods. The inclining Block Rate (IBR) scheme will increase the per-unit cost when the consumption increases. Time-of-Use (TOU) tariff method assigns a predefined rate to peak slot and off-peak slot. Day-Ahead Pricing (DAP) scheme will let the user buy or sell energy 1 day in advance before the operating day. The right choice of the pricing scheme is crucial in determining the success of any demand response scheme. A comparative analysis of the power consumption pattern of an LT domestic user is shown in Fig. 1 with an IBR pricing scheme for 2 years is depicted in Fig. 6.

Fig. 6
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Two-year energy consumption of an LT domestic user in India

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In Fig. 6, the average energy consumption of the user does not fall below 550 units (shown in Table 4) and therefore the customer has to always pay a higher bill amount due to the IBR scheme.

Table 4 Yearly average energy consumption of the LT user with a bimonthly billing period
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A smart HEM system with an appropriate dynamic pricing scheme will help such kinds of users reduce their electricity bills to a great extent. Implementing optimization schemes by combining pricing methods is a different way of enhancing DR. A TOU pricing scheme was used in Shakeri et al. [51] and also stated that as a future scope, a combination of DAP scheme and IBR may be used. Generally, a DR program is classified into incentive-based and price-based DR programs [60]. A price-based demand response model helps customers alter their energy usage patterns concerning the price information sent by a power utility company [61]. A detailed rating of a price-based DR scheme was depicted in the paper where it was found that TOU helps in better user cooperation. Considering the possibility of combining two pricing schemes, the types of price-based DR programs can be modified as in Fig. 7.

Fig. 7
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Proposed price-based demand response programs

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3.2 Types of optimization algorithms

While designing a home energy management system; optimization of load, user comfort, and energy cost should be the major focus. An attempt to optimize any of these should not indirectly harm the other factors to make a reliable HEM system. Many approaches in this regard have been undertaken and are listed here. The major highlight of the optimization strategy presented in one paper was to design a smart controller such that it incorporates a photovoltaic system and a battery as an alternative source [51]. Here new appliances are diverted to run on battery when a predefined load limit is reached rather than shifting to another time. The signal exchange was performed using XBEE technology.

In another method, the measurement of energy was based on ZigBee [33]. It also incorporated a PLC-based renewable energy gateway to oversee sustainable power generation. Here the smart home server will collect and analyze the data to organize the appliance's time of use. A different scenario proposed an intelligent cloud HEMS that assigned dynamic priority to the household appliance by the type and current status. The priority was assigned in consideration of the renewable energy capability [30].

Many stochastic approaches are also utilized for the optimization of cost. For instance, an IoT-based smart home control system using the RTP model and particle swarm optimization (PSO) technique was utilized to achieve energy cost reduction [44]. For this, a clear picture of the maximum load and its time and reason of occurrence has to be collected. The proposed method used electric vehicles as energy storage systems (ESS) and it along with an uninterruptible power supply (UPS) helped to reduce energy consumption and thereby in cost optimization.

Another way was to propose a load scheduling scheme that helped to reduce the peak load to average load ratio by combining RTP and IBR [62]. Problem modeling was performed using a genetic algorithm. The period for appliance operation was divided into a 12-min interval. The hardware implementation of a HEM system using a machine learning algorithm was proposed in Hu and Li [63] where an RTP-sensitive control approach over the home appliances was performed that used sensors to detect the human presence. Another attempt was to investigate the role of M2M communication in smart grids [64]. A dynamic programming algorithm considering the network design issue and optimal HEMS traffic concentration was proposed. Thus, the optimization method for HEM can be classified into specific algorithms and metaheuristic algorithms [65].

For cost optimization, different pricing schemes with the scope of the penalty were also adopted. An extensive and general optimization-based controller was designed in Althaher et al. [66] where the domestic appliances were categorized as curtailable, deferrable, thermal, and critical loads, and the loads scheduling was based on dynamic price signals and user comfort level. The consequence of this kind of load scheduling is that the peak load magnitude may increase at off-peak hours. An attempt to resolve this was to apply a higher price if the user demand was increased above a preset value while shifting to low priced time slot with an effort to reduce the load at the peak load pricing period.

Next, a cost minimization algorithm was proposed where each user will have an optimal start time and operating mode [53]. The algorithm was greedy in design and also introduced a penalty term in the cost function that will penalize extensive variation in merit scheduling between consecutive iterations. Another attempt suggested a simulation model that generated load usage information based on a flat tariff scheme and thereafter simulated the same load profile after equipping the circuit with the smart appliance and time-based tariff scheme [54]. Formulating dynamic costing methods and optimizing the same in a dual convex manner where IoT provided the facility to have an interactive communication channel between an electric company, the user and the power equipment was another attempt [49]. This helps in the exchange of real-time energy information.

Optimization can also be performed on single loads that consume more energy. A multi-sensing, heating, and air conditioning system was designed by utilizing Kruskal’s algorithm for preparing a new routing protocol [10]. It also surveyed the interoperability among Zigbee devices designed by various manufacturers of smart energy-enabling products. A demand-side management system with management of a refrigerator and its financial savings was demonstrated in Zehir and Bagriyanik [67] where several cooling schedules were simulated and the respective energy cost was monitored under different pricing methods.

For improving user comfort and user privacy, optimization methods were also suggested by researchers. A power management model was proposed using a rechargeable battery for smart meter privacy using a power mixing algorithm [68]. The model used three different privacy matrices. Another attempt introduced a controller based on ANFIS that can foresee the energy requirement which varied with respect to the user’s lifestyle [11]. The user can save energy with the help of the decision support system that recommends the optimal run time schedules for the home appliances. A ripple managing methodology was proposed in Qureshi et al. [12] with a modified priority circuit which if implemented can downsize the load-shedding interval, to increase user comfort. To minimize the installation engineering, a low-cost and decisive gateway controller was developed which also had plug and play facility [69]. The design was in an embedded platform and a composite operating system for high performance was developed. Memory reduction was an added advantage for the system.

Comparative performance analysis of different optimization schemes will give an insight into the right choice of adopting a HEM system. Esther and Kumar [70], surveyed various optimization techniques introduced on the demand-side as conflicting characteristics like deterministic versus stochastic and individual user versus cooperative user. A detailed survey of game theory in DSM was also conducted. Another robust optimization strategy was presented in Ghasemnejad et al. [71] that limited the ramp rate of power trading citizen energy communities and achieved 34.6% operation cost reduction. Also, optimization can be of multi-objective type [72] which mutually helps in reducing the usage cost, self-sufficiency as well as the end user comfort. In this context, an improved cockroach swarm approach was proposed in Alhasnawi et al. [73] which helped to reduce energy costs by 46.085%.

3.3 Scope of cloud-based monitoring and control of HEM system

Since we are in an era of smart technology and smart devices, home energy usage information can be easily passed to the user through smart devices like a smartphone [74]. To introduce new smart devices into the network, it is necessary to check the operational performance of various Zigbee devices and the way of data transfer [10]. To manage the use of electricity, a green HEMS was developed by Jinsoo Han, Chang-Sic Choi, Wan-Ki Park, and Ilwoo Lee wherein feedback is sent to the user of the appliance on the consumed energy; at the same time, it also compares the consumption pattern of identical appliances located in the other house but connected to the server [75]. This kind of comparison plays a vital part in demand response programs that will alleviate the energy crisis and environmental problems.

Major power wastage occurs when the appliance is in standby mode. If a room can be controlled by an IR remote, the standby power consumption may be reduced [76]. The system was designed such that, the room has an automatic standby power cutoff outlet with a light connected through a ZigBee hub. By this system, a user can identify appliances that are unnecessarily consuming power and the amount of consumption for a particular period. In the year 2009, Google introduced software and named it a power meter [77] which when used with a smart meter helped the home energy user to monitor the electricity usage. In the same year, Microsoft also launched an energy monitoring tool called Microsoft Hohm [78] which was an online web application. Apple also introduced a device that can monitor the energy consumption of a home using home plug power line networking [79]. Due to customer's lack of awareness of energy management and poor demand response, these tools never gained popularity.

By adopting the smart metering method with appropriate communication techniques, the energy consumption pattern of home appliances can be monitored to intelligently manage it [47, 80]. Thus, a HEM system can be designed by adopting different communication schemes like ZigBee, PLC, Bluetooth, Wi-Fi, Ethernet & XBee for data transfer. A survey on preferred communication schemes (Fig. 8) in the HEM system reveals that 40% of researchers preferred to use ZigBee over other data transfer schemes owing to its lower power consumption rate and low cost.

Fig. 8
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HEM system data transfer devices

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Recently most of the home networks are utilizing the scope of Wi-Fi for better data rate while using their smartphone or laptop. Hence a combination of ZigBee and Wi-Fi may yield better user comfort in the field of HEM. The complications involved while handling wireless sensor networks along with steps to generate energy by nodes were depicted in Babayo et al. [81].

In general, a HEM system can be considered to be smart and green if all the building blocks shown in Fig. 9 are considered while designing the HEM.

Fig. 9
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A smart green HEM system

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A HEM system can be considered to be smart if it can monitor end user’s emotions through behavioral management and control loads and thereby the customer amenity, as well as user privacy, is not compromised [49, 82]. If such a system also uses the backup of an alternate energy source along with a storage facility that lets the master controller intelligently assign the appliances to the appropriate energy source concerning a dynamic pricing scheme with a prevailing penalty factor during the time of operation, then the smart HEM system can be considered to be a green smart system since it lowers greenhouse gas emission by the utility company. Electric vehicle's presence as a load and storage option will be an added benefit.

4 Socio-economic responsibility: need of the hour

As the need for energy is increasing day by day, our reserves are depleting at a higher rate [83, 84]. More wastage of money is occurring due to poor demand response. An endeavor to improve it curtails the consumption of energy that can be utilized alternatively for other productive processes. A 15% reduction in the electricity bill amount was achieved in Shakeri et al. [51] which used a photovoltaic system as an auxiliary source of power supply along with smart plugs and an intelligent controller which means power consumption was decreased and so the pollution made by utility companies was also reduced. A study of socio-economic factors influencing the formation of peak loads along with its changing pattern concerning the future has to be studied for optimizing the appliance operation [44]. The optimization method proposed in the paper has a future scope of implementation in many residential sectors and appliances.

Methods to promote the production of renewable energy should be taken on the demand side [49]. A thorough understanding of the customer's readiness to shift to a dynamic tariff scheme from a flat rate tariff scheme has to be made which will also give an insight into its environmental and social impact [59]. The HEMS system with slighter modifications finds application in other sectors also. For example, the home gateway controller system can be used for other sectors like medical treatment systems and security systems [69]. Few HEM strategies create new employment opportunities like electricity aggregators [63] who may work as agents between consumers and the power utility company. The involvement of such aggregators will boost the overall system performance [85, 86].

The need for a power system upgrade can be postponed by lowering the peak demand which will, in turn, improve the system reliability. Demand response over the long term will eventually reduce the overall plant and capital cost investment and thereby economic sustainability can be achieved. Almost 37.9% of a refrigerator load demand during peak load can be postponed to other intervals and thereby 11.4% of annual energy cost can be reduced [67]. If this is possible, then by framing a supervision algorithm for multiple refrigerators, more savings could be made.

User privacy is another socially relevant pain area that has to be taken care of. Nobody wishes to share data related to their privacy like when they do the laundry, how many times they switch on the television, at what temperature they have set their air conditioner, etc. A privacy protection algorithm that masked the energy consumption instances for better user privacy was introduced in Kalogridis et al. [87] that filtered the energy usage information and demonstrated how the information-theoretic anonymity metrics can be applied to analyze the user privacy protection supported by water filling power transformation algorithm. The paper also proposed that the privacy of a particular set of appliances can be protected with the help of a rechargeable battery.

While designing a smart HEM system extra care should be taken to consider the differently-abled and elderly people since their needs are a little bit different from a normal user. An economic home automation system that was equipped with wireless remote control for the disabled and elderly category was proposed in Ramlee et al. [88]. The GUI was designed on the Android platform for smartphones and Windows OS for PCs or laptops. Using low-voltage activating switches improves safety.

In India, the existing energy framework mostly focuses on implementing DSM initiatives through peak load reduction [89]. The nation in an attempt for better energy management and greenhouse gas reduction; has been reformulating the policies and laws to popularize DSM in rural areas. In the model DSM regulations at the state level (2017), guidelines were formulated for the purchasers as well as sellers of electricity for short-term, medium-term, and long-term access to intra-state and inter-state energy transmission and distribution [90] to maintain grid control and security.

The state governments in association with the central government are also reformulating their existing policy and laws to increase the count of implementation of renewable power projects. For instance, the electricity department of the state of Kerala, India has formulated different financial saving models [91] for encouraging home energy users to install rooftop solar power systems which help to lower their electricity bills. The first model lets the user consume a portion of the generated energy and the rest be fed to the utility company owned by the government. The amount of generated energy allowed for self-consumption depends on the percentage of the financial contribution made by the consumer for plant installation. The second model provides a financial subsidy for the total plant installation cost and the percentage of financial subsidy depends upon the total kilowatts peak of the solar power plant installed. In another effort to reduce the energy price, India has also adopted the e-reverse auction mechanism [92] where the energy bidders will get an opportunity to rebid after the disclosure of the lowest ongoing bid. The Multi-Carrier Energy Systems integration is another approach to improve the system stability [93]. More recently the concept of smart community energy storage has been gaining popularity as it helps in reducing energy costs with little disturbance to end-user comfort [94,95,96].

From the above discussions, it is clear that a successful home energy management strategy requires a multiple pre-evaluation approach that helps to understand the basic needs of an end user as well as a power utility company. While most of the lifestyle surveys focused on identifying the energy consumption pattern and time, care should also be taken to find out how effectively the existing pricing scheme helps the consumer to reduce energy consumption and bill. Figure 6, clearly outlines the negative impact of the existing billing scheme. While different communication schemes are available for home energy management, Zigbee was preferred by most of the researchers when compared with other schemes like Bluetooth, PLC, Wi-Fi, Ethernet, and XBee. It is also understood that most of the home energy management focuses on reducing energy consumption and improving general user comfort, very few attempts are made to improve the user comfort of elderly people and differentially abled people. As the load consumption is increasing each year (Fig. 1), it is clearly understood that an improved pricing scheme proposed in Fig. 7 is to be implemented for a better demand response. As most of the researchers focus on utilizing solar and wind energy in India, biopower which is the third greatest energy source (Fig. 4) is not properly utilized in India.

5 Conclusion

As newer technologies and products are introduced into the energy market with the goal of the betterment of humankind, the demand for energy in any form is also increasing. For instance, a 26% rise in energy consumption occurred for the consumer shown in Fig. 2. Novel techniques are available that can efficiently harvest and utilize renewable energy. In this scenario, a proper home energy management system will help to yield a good demand response thereby saving a lot of energy that can be productively utilized for other sectors of the nation. A simple reengineering of lighting loads of 200 LT domestic consumers will save 4.68 MWh of energy in a year. Most of the demand management systems in India focus on changing from a flat rate-based tariff system to a time-based pricing tariff system. This attempt alone may not encourage the user to invest in smart appliances and other DSM methods since their return from their capital investment might not be very satisfying. The utility company should give some relaxation to the users who are willing to migrate to a DSM-based platform incorporating RTP, with a more reduced tariff scheme or any other kind of privileges that may attract them to implement DSM.

It should be noted that the user's comfort is violated if a DSM system forces the consumer to produce a flat load profile and compelling to stick to that load profile during the time of actual consumption using penalty. Hence while designing a home energy management system, the prior concern should be given to user comfort and user privacy. The existing energy policies and laws of the country or the community should be designed in such a way that it should motivate the consumer to integrate renewable technologies into their HEM system which helps in reducing their usage cost. This alternately helps the power utility company to reduce their maximum load demand at the power station and thereby the need for generation expansion can be avoided or reduced to a great extent.

In rural areas of India, biomass is an excellent source of alternate energy that can be converted into electricity and used during night hours or periods when solar energy is intermittent. As a future scope, deploying an On-Grid solar power network along with biomass energy into the grid and utilizing the potential of a combined real-time pricing scheme and conventional pricing scheme will eventually benefit the user and utility company with less capital investment on smart appliances.