Abstract
Load forecasting is the main exploration field in the smart grid technologies. The classification of load forecasting depends on its target forecasting that ranges from minutes to years. Residential smart home load forecasting focuses on forecasting energy consumption of smart homes which is crucial when it comes to energy conservation and load management issues. This paper focuses on application and implementation of deep learning algorithm known as long short-term memory (LSTM) that predicts the load of residence hours or days ahead and the time series load analysis of the houses will be presented.
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Araya, S., Rakesh, N., Kaur, M. (2021). Smart Home Load Analysis and LSTM-Based Short-Term Load Forecasting. In: Singh, P.K., Polkowski, Z., Tanwar, S., Pandey, S.K., Matei, G., Pirvu, D. (eds) Innovations in Information and Communication Technologies (IICT-2020). Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-66218-9_14
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DOI: https://doi.org/10.1007/978-3-030-66218-9_14
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