ProLoaF: Probabilistic load forecasting for power systems

Today, the energy supply does not follow the demand in a controlled manner anymore. Thus, forecasting the electricity consumption became essential for the operation of power systems. Already numerous open source software tools exist that provide forecasting models, which are configurable for different forecasting tasks. In the case of electrical energy demand, a change in the geographical or temporal settings, requires specific domain knowledge on relevant data and influencing factors that are to be considered when developing data-driven forecasting models. With ProLoaF, we propose a holistic machine-learning based forecasting project, which offers the developer a continuous deployment of reliable forecasts for the power system domain. ProLoaF serves for probabilistic forecasts of the electric energy consumption and non-controllable generation in future power system operation. By overlapping Machine Learning (ML), DevOps and power systems engineering disciplines, we aim to accelerate future forecasting model development by reducing consultation work between domain experts.& COPY; 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).