Development of Energy Consumption Profiles of Common Household Appliances by Analyzing their Energy Consumption

Authors

  • Rajat Nainwal National Institute of Technology Hamirpur
  • Dr. Aniket Sharma

DOI:

https://doi.org/10.15320/ICONARP.2025.333

Keywords:

Alternate Energy Source, Energy consumption profiles, Energy efficiency, Energy management, Sustainable Development Goals

Abstract

Residential energy consumption constitutes a major share of overall electricity demand, and inefficient use of household appliances, including hidden standby loads, which contributes to higher energy costs, wasted resources, and barriers to sustainable energy management. Addressing this requires accurate insights into how appliances consume energy in real-time, enabling more efficient strategies for household, utilities, and policymakers. This study conducted a comprehensive analysis of residential energy consumption by monitoring the real-time energy usage of common household appliances. The primary goal was to develop detailed energy consumption profiles that could benefit both researchers and distribution companies. To achieve this, the energy consumption data of various household appliances were recorded over a period of one month with a high time resolution of one-second intervals, utilizing smart plugs for wireless energy measurement. A significant focus of the study was to understand the impact of standby power consumption on overall energy use and efficiency. By accurately measuring appliance-level energy consumption, the study was able to create detailed profiles, which were then used to predict the energy use for the following month. The predicted total monthly energy consumption was validated against actual energy bills provided by the state electricity board, demonstrating the reliability and accuracy of the predictions. The collected data from this study offers a valuable database for identifying and understanding energy consumption patterns of household appliances, which is essential for residential energy management research. Further, the findings emphasize the significance of real-time monitoring in crafting effective energy management strategies. Such strategies can lead to more sustainable energy use, benefiting both consumers and energy providers. On a broader scale, this method can support economic development by enhancing energy efficiency and reducing waste. The study underscores the potential of detailed, real-time energy monitoring to improve energy policy and household energy management, paving the way for more informed and sustainable energy practices.  

References

Abeykoon, V., Kankanamdurage, N., Senevirathna, A., Ranaweera, P., & Udawalpola, R. (2016). Electrical devices identification through power consumption using machine learning techniques. International Journal of Simulation: Systems, Science and Technology, 17(32), 1–9. https://doi.org/10.5013/IJSSST.a.17.32.13

Agnetis, A., De Pascale, G., Detti, P., & Vicino, A. (2013). Load scheduling for household energy consumption optimization. IEEE Transactions on Smart Grid, 4(4), 2364–2373. https://doi.org/10.1109/TSG.2013.2254506

Ahmed, M. S., Mohamed, A., Homod, R. Z., Shareef, H., Sabry, A. H., & Bin Khalid, K. (2015). Smart plug prototype for monitoring electrical appliances in Home Energy Management System. 2015 IEEE Student Conference on Research and Development, SCOReD 2015, 32–36. https://doi.org/10.1109/SCORED.2015.7449348

Ajay-D-Vimal Raj, P., Sudhakaran, M., & Philomen-D-Anand Raj, P. (2009). Estimation of standby power consumption for typical appliances. Journal of Engineering Science and Technology Review, 2(1), 71–75. https://doi.org/10.25103/jestr.021.14

Angioni, A., Schlösser, T., Ponci, F., & Monti, A. (2016). Impact of pseudo-measurements from new power profiles on state estimation in low-voltage grids. IEEE Transactions on Instrumentation and Measurement, 65(1), 70–77. https://doi.org/10.1109/TIM.2015.2454673

Beaudin, M., & Zareipour, H. (2015). Home energy management systems: A review of modelling and complexity. Renewable and Sustainable Energy Reviews, 45, 318–335. https://doi.org/10.1016/j.rser.2015.01.046

Bennett, C. J., Stewart, R. A., & Lu, J. W. (2014). Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system. Energy, 67, 200–212. https://doi.org/10.1016/j.energy.2014.01.032

Bissey, S., Jacques, S., & Le Bunetel, J. C. (2017). The fuzzy logic method to efficiently optimize electricity consumption in individual housing. Energies, 10(11). https://doi.org/10.3390/en10111701

Boegle, A., Singh, D., & Sant, G. (2010). Energy Saving Potential in Indian Households from Improved Appliance Efficiency. In Prayas Energy Group (pp. 1–36). Prayas Energy Group. https://prayaspune.org/girish-sant/images/pdf/energy_saving_potential_from_indian_households_from_appliance_efficiency_108a01.pdf

Capasso, A., Lamedica, R., Prudenzi, A., & Grattieri, W. (1994). A bottom-up approach to residential load modeling. IEEE Transactions on Power Systems, 9(2), 957–964. https://doi.org/10.1109/59.317650

Cetin K. S., Tabares-Velasco, P. C., & Novoselac, A. (2014). Appliance daily energy use in new residential buildings: Use profiles and variation in time-of-use. Energy and Buildings, 84(December), 716–726.

Chuan, L., & Ukil, A. (2015). Modeling and Validation of Electrical Load Profiling in Residential Buildings in Singapore. IEEE Transactions on Power Systems, 30(5), 2800–2809. https://doi.org/10.1109/TPWRS.2014.2367509

Csoknyai, T., Legardeur, J., Akle, A. A., & Horváth, M. (2019). Analysis of energy consumption profiles in residential buildings and impact assessment of a serious game on occupants’ behavior. Energy and Buildings, 196, 1–20. https://doi.org/10.1016/j.enbuild.2019.05.009

Czétány, L., Vámos, V., Horváth, M., Szalay, Z., Mota-Babiloni, A., Deme-Bélafi, Z., & Csoknyai, T. (2021). Development of electricity consumption profiles of residential buildings based on smart meter data clustering. Energy and Buildings, 252. https://doi.org/10.1016/j.enbuild.2021.111376

Efficiency, B. of E. (2020). Star Label Homepage of Bureau of Energy Efficiency. https://beeindia.gov.in/star-label.php

Ekholm, T., Krey, V., Pachauri, S., & Riahi, K. (2010). Determinants of household energy consumption in India. Energy Policy, 38(10), 5696–5707. https://doi.org/https://doi.org/10.1016/j.enpol.2010.05.017

Energy Statistics India (pp. 10–12). (2022). Ministry of Statistics and Program Implementation, Government Of India. https://mospi.gov.in/web/mospi/reports-publications/-/reports/view/templateFive/27201?q=RPCAT

Faruqui, A., & Sergici, S. (2010). Household response to dynamic pricing of electricity: A survey of 15 experiments. Journal of Regulatory Economics, 38(2), 193–225. https://doi.org/10.1007/s11149-010-9127-y

Firth, S., Lomas, K., Wright, A., & Wall, R. (2008). Identifying trends in the use of domestic appliances from household electricity consumption measurements. Energy and Buildings, 40(5), 926–936. https://doi.org/10.1016/j.enbuild.2007.07.005

Hong, J. H., Hong, D. Y., Yao, L. H., & Fu, L. C. (2020). A demand side management with appliance controllability analysis in smart home. Proceedings -of the 2020 International Conference on Smart Grids and Energy Systems, SGES 2020, 556–561. https://doi.org/10.1109/SGES51519.2020.00104

Hurley, D., Peterson, P., & Whited, M. (2013). Demand Response as a Power System Resource. Synapse Energy Economics Inc, May, 81. http://www.synapse-energy.com/Downloads/SynapseReport.2013-03.RAP.US-Demand-Response.12-080.pdf

International Energy Agency. (2017). World Energy Outlook.

Kam, M., Suryadevara, N. K., Mukhopadhyay, S. C., & Gill, S. P. S. (2014). WSN based utility System for effective monitoring and control of household power consumption. Conference Record - IEEE Instrumentation and Measurement Technology Conference, 1382–1387. https://doi.org/10.1109/I2MTC.2014.6860973

Kuzlu, M., Pipattanasomporn, M., & Rahman, S. (2012). Hardware demonstration of a home energy management system for demand response applications. IEEE Transactions on Smart Grid, 3(4), 1704–1711. https://doi.org/10.1109/TSG.2012.2216295

Marina, L., Masi, R.-F. de, Karatasou, S., Santamouris, M., & Assimakopoulos, M.-N. (2022). On the impact of user behaviour on heating energy consumption and indoor temperature in residential buildings. Energy and Buildings, 255(January), 111657.

Maurya, S., CGS, G., Garg, V., & Mathur, J. (2023). Summer Electricity Consumption Patterns in Households Using Appliance Load Profiles. Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 485–490. https://doi.org/https://doi.org/10.1145/3600100.3627027

Meier, A. K. (2001). A worldwide review of standby power use in homes. In Lawrence Berkeley National Laboratory (DE-AC03-76SF00098). http://escholarship.org/uc/item/03m799xz.pdf

Nainwal, R., & Sharma, A. (2024). Comparison of energy prediction models for residential buildings: a case study in Himachal Pradesh, India. Progress in Energy, 6(4). https://doi.org/10.1088/2516-1083/ad87a3

Paatero, J. V., & Lund, P. D. (2006). A model for generating household electricity load profiles. International Journal of Energy Research, 30(5), 273–290. https://doi.org/10.1002/er.1136

Pipattanasomporn, M., Kuzlu, M., Rahman, S., & Teklu, Y. (2014). Load profiles of selected major household appliances and their demand response opportunities. IEEE Transactions on Smart Grid, 5(2), 742–750. https://doi.org/10.1109/TSG.2013.2268664

Santin, O. G. (2011). Behavioural Patterns and User Profiles related to energy consumption for heating. Energy and Buildings, 43(10), 2662–2672.

Shakeri, M., Shayestegan, M., Abunima, H., Reza, S. M. S., Akhtaruzzaman, M., Alamoud, A. R. M., Sopian, K., & Amin, N. (2017). An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy and Buildings, 138, 154–164. https://doi.org/10.1016/j.enbuild.2016.12.026

Shirazi, E., Zakariazadeh, A., & Jadid, S. (2015). Optimal joint scheduling of electrical and thermal appliances in a smart home environment. Energy Conversion and Management, 106, 181–193. https://doi.org/10.1016/j.enconman.2015.09.017

Sustainable Development Goals, Department of Economic and Social Affairs, United Nations. (2015). https://sdgs.un.org/goals

Tang, S., Kalavally, V., Ng, K. Y., & Parkkinen, J. (2017). Development of a prototype smart home intelligent lighting control architecture using sensors onboard a mobile computing system. Energy and Buildings, 138, 368–376. https://doi.org/10.1016/j.enbuild.2016.12.069

Tewathia, N. (2014). Determinants of the household electricity consumption: A case study of Delhi. International Journal of Energy Economics and Policy, 4(3), 337–348.

Wu, X., Hu, X., Yin, X., & Moura, S. J. (2018). Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage. IEEE Transactions on Smart Grid, 9(3), 2065–2075. https://doi.org/10.1109/TSG.2016.2606442

Xu, Z., Gao, Y., Hussain, M., & Cheng, P. (2020). Demand Side Management for Smart Grid Based on Smart Home Appliances with Renewable Energy Sources and an Energy Storage System. Mathematical Problems in Engineering, 2020, 1–20. https://doi.org/10.1155/2020/9545439

Zhang, D., Shah, N., & Papageorgiou, L. G. (2013). Efficient energy consumption and operation management in a smart building with microgrid. ENERGY CONVERSION AND MANAGEMENT, 74, 209–222. https://doi.org/10.1016/j.enconman.2013.04.038

Published

31-12-2025

How to Cite

Nainwal, R., & Sharma , D. A. (2025). Development of Energy Consumption Profiles of Common Household Appliances by Analyzing their Energy Consumption . ICONARP International Journal of Architecture and Planning, 13(2), 494 – 523. https://doi.org/10.15320/ICONARP.2025.333

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Section

Articles