Citation

Young, Melvin Tuck Wai and Day, Chyi Ku and Chong, Siang Yew (2025) Machine Learning in Solar Energy Cost Optimisation. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

By shifting the maximum demand from peak rate periods to mid-peak or off-peak rate tariffs, customers can lower their monthly electricity costs by several hundred to several thousand ringgit. With the help of machine learning using historical data to predict load consumption and solar energy generation for the next 7 days with an interval of 30 minutes, users can recognise when they should charge and discharge their energy storage to avoid maximum demand at peak rate especially on rainy days. This research uses all the available machine learning models in an automated machine learning library for time series prediction to identify the well performing models for weighted ensembles. In addition, the historical data is sorted by month and calculated with the help of a tariff library. This allows different users to decide which tariff scheme they can use to reduce their electricity bill when using solar energy and energy storage.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Electricity, load forecasting, machine learning, maximum demand, solar energy
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK452-454.4 Electric apparatus and materials. Electric circuits. Electric networks
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	19 Mar 2026 01:54
Last Modified:	19 Mar 2026 01:54
URII:	http://shdl.mmu.edu.my/id/eprint/15606

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