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Deep learning based predictive analysis of energy consumption for smart homes

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Abstract

Predicting energy consumption has become crucial to creating a sustainable and intelligent environment. With the aid of forecasts of future demand, the distribution and production of energy can be optimized to meet the requirements of a vastly growing population. However, because of the varied types of energy consumption patterns, predicting the demand for any household can be difficult. It has recently gained popularity with social Internet of Things-based smart homes, smart grid planning, and artificial intelligence-based smart energy-saving solutions. Although there are methods for estimating energy consumption, most of these systems are based on one-step forecasting and have a limited forecasting period. Several prediction models were implemented in this paper to address the problem mentioned above and achieve high accuracy, including the baseline model, the Auto-Regressive Integrated Moving Average (ARIMA) model, the Seasonal Auto-Regressive Integrated Moving Average (SARIMAX with eXogenous factors) model, the Long Short-Term Memory (LSTM) Univariate model, and the LSTM Multivariate model.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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The authors declare that they do not have any funding or grant for the manuscript.

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Authors and Affiliations

  1. Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Sangeeta Malik, Ishmeet Singh, Harsh Vardhan Gupta & Sidhant Prakash

  2. IT Department, Maharaja Surajmal Institute of Technology, New Delhi, India

    Sitender Malik

  3. IT Department, Bhagwan Parshuram Institute of Technology, New Delhi, India

    Rachna Jain

  4. Department of Computer Engineering-AI & BD, Marwadi University, Rajkot, Gujarat, India

    Biswaranjanjan Acharya

  5. Department of Computer Science, TungHai University, No. 1727, Sec. 4, Taiwan Boulevard, Xitun District, 407224, Taichung City, Taiwan, Republic of China

    Yu-Chen Hu

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  1. Sangeeta Malik
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Correspondence to Yu-Chen Hu.

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Malik, S., Malik, S., Singh, I. et al. Deep learning based predictive analysis of energy consumption for smart homes. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18758-z

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