Abstract
Building consumptions has been the focus of numerous researchers since the introduction of building energy with zero carbon. However, many researches have indicated that excessive use of energy to produce comfort leads to problem associated with indoor air quality like high level of indoor CO2. It is therefore essential to create a strategy to improve this condition. This paper presented both investigation and prediction regarding strategies to indoor CO2 reduction as energy-efficient approaches. The research was consisted of two stages, in which the first stage was to monitor the existing of indoor CO2 level and reduce it when exceed the standard. Whereas, the second stage was to predict the level of indoor CO2. An approach of indoor CO2 monitoring was carried out by smart sensor system through Arduino UNO Microcontroller and an Artificial Neural Network was involved to predict the level of indoor CO2 during the application of smart sensor system. The results indicated that the application of smart sensor system was remarkable in diminishing indoor CO2. Besides, the use of Artificial Neural Network involved network architecture of 4–30–30 with learning rate initial of 0.7 and activation function of relu. The model showed positive correlation which characterized by R2 value that was 0.599. Ultimately, this research found the correlation between energy-efficient consumption in building and a strategy to reduce indoor CO2 in order to create comfort and healthy environment. In addition, keep bear in mind that is important to find out the connection between the indoor environmental and its surroundings to create positive ambience that leads to sustainable development.
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References
Billionnet C, Sherrill D, Maesano IA (2012) Estimating the health effects of exposure to multi-pollutant mixture. Ann Epidemiol 22(2)
Kosonen R, Tan F (2004) The effect of perceived indoor air quality on productivity loss. Energy Build 36:981–986
Wargocki P, Wyon DP, Fanger PO (2000) Productivity is affected by the air quality in offices. Healthy Build 1:635–640
Bluyssen PM (2009) Towards an integrative approach of improving indoor air quality. Build Environ 44(9):1980–1989
Bluyssen PM (2004) A clean and energy-efficient heating, ventilating and air-conditioning system: recommendations and advice. TNO, The Netherlands. ISBN 90-5986-009-8
Zhao P, Siegel JA, Corsi RL (2007) Ozone removal by HVAC filters. Atmos Environ 41:3151–3160
Sofuoglu SC (2008) Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings. Build Environ 43:1121–1126
Xie H, Ma F, Bai Q (2009) Prediction of indoor air quality using artificial neural networks. In: 2009 fifth international conference on natural computation, pp 414–418
Azid A, Juahir H, Toriman ME, Kamarudin MKA, Saudi ASM, Hasnam CNC, Aziz NAA, Azaman F, Latif MT, Zainuddin SFM, Osman MR, Yamin M (2014) Prediction of the level of air pollution using principal component analysis and artificial neural network. A case study in Malaysia. Water Air Soil Pollut
Liu Z, Cheng K, Li H, Cao G, Wu D, Shi Y (2017) Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi : a combined experimental and neural network modeling study. Environ Sci Pollut Res
Putra JCP, Safrilah, Ihsan M (2018) The prediction of indoor air quality in office room using artificial neural network. In: Human-dedicated sustainable product and process design: materials, resources, and energy—AIP conference proceedings, vol 20040
Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5(4):373–401
Wang JZ, Wang JJ, Zhang ZG, Guo SP (2011) Expert systems with applications forecasting stock indices with back propagation neural network. Expert Syst Appl 38(11):14346–14355
Liu Z, Liu K, Li H, Zhang X, Jin G, Cheng K (2015) Artificial neural networks-based software for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters. PLoS One 1–16
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Putra, J.C.P., Susanto, T. (2020). Artificial Neural Network Modelling of Indoor CO2 Reduction as Energy-Efficient Strategies. In: Sabino, U., Imaduddin, F., Prabowo, A. (eds) Proceedings of the 6th International Conference and Exhibition on Sustainable Energy and Advanced Materials. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4481-1_66
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DOI: https://doi.org/10.1007/978-981-15-4481-1_66
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