Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
Chatterjee S, Ramachandran A, Neergaard TF, Maier A, Bayer S (2022)
Publication Language: English
Publication Status: Submitted
Publication Type: Conference contribution, Original article
Future Publication Type: Conference contribution
Publication year: 2022
Publisher: NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning
Conference Proceedings Title: NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning
Event location: Hybrid
URI: https://www.climatechange.ai/papers/neurips2022/46
Open Access Link: https://www.climatechange.ai/papers/neurips2022/46
Abstract
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417 kW for RMSE is achieved with the proposed framework in comparison to all other methods.
Authors with CRIS profile
Adithya Ramachandran
Lehrstuhl für Informatik 5 (Mustererkennung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Siming Bayer
Lehrstuhl für Informatik 5 (Mustererkennung)
Related research project(s)
How to cite
APA:
Chatterjee, S., Ramachandran, A., Neergaard, T.F., Maier, A., & Bayer, S. (2022). Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble. In NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning. Hybrid, US: NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning.
MLA:
Chatterjee, Satyaki, et al. "Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble." Proceedings of the NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning, Hybrid NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning, 2022.
BibTeX: Download