Abstract:
The transition to high shares of renewables leads to high demand for flexibility in the energy system. Due to increasing use of heat pumps and electric cars, the resident...Show MoreMetadata
Abstract:
The transition to high shares of renewables leads to high demand for flexibility in the energy system. Due to increasing use of heat pumps and electric cars, the residential sector becomes more and more relevant in this respect. A precondition for reliably providing flexibility is the detailed knowledge of the current and future electricity demand of the respective household, which becomes possible with the comprehensive rollout of smart metering systems. Since the load of individual households is highly volatile, probabilistic forecast methods-based on point prediction and direct interval prediction-are considered. The evaluations reveal that independently of the method, high interval widths result due to very volatile load patterns. All developed point prediction methods outperform the benchmark. The computation time can be reduced significantly by direct interval forecasting while maintaining comparable accuracy.
Date of Conference: 29 September 2019 - 02 October 2019
Date Added to IEEE Xplore: 21 November 2019
ISBN Information: