Chatterjee S, Bayer S, Maier A (2021)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2021
City/Town: https://www.climatechange.ai/events/neurips2021.html#accepted-works
Conference Proceedings Title: Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021
Event location: Online
URI: https://www.climatechange.ai/events/neurips2021.html#accepted-works
Open Access Link: https://www.climatechange.ai/papers/neurips2021/42/paper.pdf
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.
APA:
Chatterjee, S., Bayer, S., & Maier, A. (2021). Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model. In Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021. Online: https://www.climatechange.ai/events/neurips2021.html#accepted-works.
MLA:
Chatterjee, Satyaki, Siming Bayer, and Andreas Maier. "Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model." Proceedings of the Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021, Online https://www.climatechange.ai/events/neurips2021.html#accepted-works, 2021.
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