Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model

• URL: http://arxiv.org/abs/2112.01908v1
• Date: Fri, 3 Dec 2021 13:46:16 GMT
• Title: Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model
• Authors: Satyaki Chatterjee, Siming Bayer, and Andreas Maier
• Abstract summary: We propose a forecasting framework for thermal energy consumption within a district heating system (DES) based on kernel Support Vector Regression (kSVR) using real-world smart meter data. The average MAPE is reduced to 2.07% and 2.64% for the individual meter-specific forecasting and for forecasting of societal consumption, respectively.
• Score: 5.3580471186206005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable. As a consequence, an accurate forecast of heat consumption on the consumer side poses an important first step towards an optimal energy supply. However, due to the non-linearity and non-stationarity of heat consumption data, the prediction of the thermal energy demand of DES remains challenging. In this work, we propose a forecasting framework for thermal energy consumption within a district heating system (DHS) based on kernel Support Vector Regression (kSVR) using real-world smart meter data. Particle Swarm Optimization (PSO) is employed to find the optimal hyper-parameter for the kSVR model which leads to the superiority of the proposed methods when compared to a state-of-the-art ARIMA model. The average MAPE is reduced to 2.07% and 2.64% for the individual meter-specific forecasting and for forecasting of societal consumption, respectively.

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