Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
This work addresses the problem of efficient and low-emission heat supply for utility companies, representing an incremental improvement in forecasting accuracy.
The paper tackles heat demand forecasting for utility companies by proposing a neural network framework that encodes time series as scalograms and incorporates exogenous variables like weather and holidays, achieving a minimal mean error of 7.54% MAPE and 417kW RMSE, outperforming state-of-the-art methods on real-world data from Denmark.
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 such as weather, and holiday/non-holiday. 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 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.