A Comparative Study of Methods to Forecast Domestic Energy Consumption Aggregated with Photovoltaic and Heat Pumps System

Rafi, Arqam and Lee, Timothy and Wu, Wenyan (2021) A Comparative Study of Methods to Forecast Domestic Energy Consumption Aggregated with Photovoltaic and Heat Pumps System. In: 26th International Conference on Automation and Computing, 2nd - 4th September 2021, Portsmouth, UK.

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Abstract

Rise in the usage of photovoltaic (PV) system at the residential sector brought challenges for the distribution network operator (DNO) and with a high demand of the Heat Pump (HP) system to fulfil the target of low carbon emission potentially brought far greater trials to predict energy at the domestic network. Prediction is very crucial for electrical distribution companies since their business largely relays on how to make the most out of their energy generation without making it go to waste. This study compares different methods (from machine learning to deep learning) to forecast domestic energy consumption aggregated with HP and PV system. The prediction tool proudly uses large residential energy measured data at a minute frequency for a year combines synthetically with the real measured data of HP and PV system. The forecasting methods is different for various data type, this study allows to compare which one would be more efficient in which type of data set and which one to predicts the finest.

Item Type: Conference or Workshop Item (Paper)
Date: 11 June 2021
Uncontrolled Keywords: esidential Energy; Predictive Analytics; Machine Learning; Large Data; Low carbon technologies; Heat pumps; Photovoltaic system
Subjects: CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific)
CAH13 - architecture, building and planning > CAH13-01 - architecture, building and planning > CAH13-01-02 - building
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Timothy Lee
Date Deposited: 17 Sep 2021 08:36
Last Modified: 17 Sep 2021 08:36
URI: http://www.open-access.bcu.ac.uk/id/eprint/12184

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