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 optimized supply of thermal energies through proactive techniques such as load forecasting. In this research thesis, we propose a deep learning-based forecasting framework for heat demand based on neural networks where the time series are represented 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, and two variants of the proposed method.
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 7.69% for Mean Absolute Percentage Error and 202.80kW for Root Mean Squared Error is achieved with the proposed framework in comparison to all other methods. Moreover, the proposed method exhibits minimal deviation of performance across different climatic seasons and geographical zones compared to the baseline methods.
This thesis is part of the “UtilityTwin” project.