Visible to the public Electrical Load Forecasting Utilizing an Explainable Artificial Intelligence (XAI) Tool on Norwegian Residential Buildings

TitleElectrical Load Forecasting Utilizing an Explainable Artificial Intelligence (XAI) Tool on Norwegian Residential Buildings
Publication TypeConference Paper
Year of Publication2022
AuthorsHenriksen, Eilert, Halden, Ugur, Kuzlu, Murat, Cali, Umit
Conference Name2022 International Conference on Smart Energy Systems and Technologies (SEST)
KeywordsBiological system modeling, Buildings, Electrical Load Forecasting, Explainable Artificial Intelligence (XAI), load forecasting, power markets, Predictive models, pubcrawl, renewable energy sources, Residential Buildings, resilience, Resiliency, Scalability, Smart grids, xai
AbstractElectrical load forecasting is an essential part of the smart grid to maintain a stable and reliable grid along with helping decisions for economic planning. With the integration of more renewable energy resources, especially solar photovoltaic (PV), and transitioning into a prosumer-based grid, electrical load forecasting is deemed to play a crucial role on both regional and household levels. However, most of the existing forecasting methods can be considered black-box models due to deep digitalization enablers, such as Deep Neural Networks (DNN), where human interpretation remains limited. Additionally, the black box character of many models limits insights and applicability. In order to mitigate this shortcoming, eXplainable Artificial Intelligence (XAI) is introduced as a measure to get transparency into the model's behavior and human interpretation. By utilizing XAI, experienced power market and system professionals can be integrated into developing the data-driven approach, even without knowing the data science domain. In this study, an electrical load forecasting model utilizing an XAI tool for a Norwegian residential building was developed and presented.
DOI10.1109/SEST53650.2022.9898500
Citation Keyhenriksen_electrical_2022