Abstract
Electricity energy billing system is prevalent in most of the places in the world. Also digitization of these electricity bills has also been successfully implemented in various underdeveloped countries as well. The vast amount of data is available regarding the energy consumption of consumers. In this paper we consider a case study of one city, about which we have electricity energy data for several years. We first classify consumers based on their average energy usage via clustering algorithms. We also have survey data of several houses. In that survey, we have building information, family information and also appliance information. We use various regression techniques to disaggregate the energy usage corresponding to various appliances.
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Acknowledgement
The author would like to thank TEQIP-III for the support for this project. Part of the work was carried out when the author was project associate at IISc Bangalore. The data was obtained from KSEB under the joint project of IISc Bangalore and Clytics Pvt. Ltd. Their support and permission to use the data is highly acknowledged.
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Shah, S.M. (2021). Modelling Energy Consumption of Domestic Households via Supervised and Unsupervised Learning: A Case Study. In: Thampi, S.M., Piramuthu, S., Li, KC., Berretti, S., Wozniak, M., Singh, D. (eds) Machine Learning and Metaheuristics Algorithms, and Applications. SoMMA 2020. Communications in Computer and Information Science, vol 1366. Springer, Singapore. https://doi.org/10.1007/978-981-16-0419-5_13
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DOI: https://doi.org/10.1007/978-981-16-0419-5_13
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