The true value of a forecast: Assessing the impact of accuracy on local energy communities. (March 2023)

• Record Type: Journal Article
• Authors: Putz, Dominik; Gumhalter, Michael; Auer, Hans
• Is Part Of: Sustainable energy, grids and networks. Volume 33(2023)
• Publication Date: 2023-03
• Subjects: Machine learning -- Forecasting -- Model predictive control -- Building asset optimisation -- Energy community -- Energy management systems -- Assessing forecasting models; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2022.100983
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: Energy communities have become a key component of growing smart grids that integrate distributed renewable energy resources, energy storage technologies, and load management techniques. The random nature of the weather causes challenges for the reliability, power quality, and supply–demand balance of such microgrids. Therefore, energy demand forecasts are increasingly crucial for the effective and continuous operation of the power grid. They also aid in achieving the best possible use of resources to push the limits of self-sufficiency. This study examines not only the quality but the so-called value of a forecast from the point of choosing a forecasting approach. Usually, forecasting approaches are ranked using quality metrics, such as the mean absolute percentage error (MAPE) or root mean square error (RMSE). In addition, the value of a forecast is considered in this study by measuring concrete results for a local energy community (LEC), such as the load cover factor, supply cover factor, on-site energy ratio, and cost of electricity. These evaluations are based on a model of an LEC that includes not only the electric components but also a building and a selected heat pump system for space heating and cooling that is fully dynamical. The optimal operation of this exemplary LEC and the integration of demand forecasts for electricity and domestic hot water (DHW) are achieved with model predictive control (MPC). Several relevant studies on management in LEC are available, but almost none of the publications examine demand forecasting strategies and optimal building asset optimisation at the same time. This research provides two major contributions: first, by developing a more comprehensive framework to assess forecasting performance with reference to energy communities; and second, by highlighting the connection between quality and value indicators of forecasts in the context of energy communities. This study's findings show that depending solely on quality metrics when choosing a forecasting approach is insufficient and gives no clear statement about the true value of a forecast. This paper identifies the impact of more accurate forecasts on energy community performance measures and attempts to provide an outlook on theoretically achievable improvements based on significantly better forecasts. Finally, this work highlights several open research issues and prospects. Highlights: Assessing forecast quality and value for load and domestic hot water demand. Grey-box model of a dynamic thermal building model based on linear programming. forecasts of higher accuracy do not necessarily lead to increased value. Using value metrics increase confidence when selecting a forecasting model.