Net load forecasting using different aggregation levels

• Published in: Energy Informatics 2022-09, Vol.5 (Suppl 1), p.1-21, Article 19
• Main Authors: Beichter, Maximilian, Phipps, Kaleb, Frysztacki, Martha Maria, Mikut, Ralf, Hagenmeyer, Veit, Ludwig, Nicole
• Format: Article
• Language: English
• Subjects: Agglomeration; Aggregation; Balancing; Computer Science; Economic forecasting; Electric power grids; Electricity distribution; Energy demand; Forecasting; Information Systems and Communication Service; Mathematical models; Net load forecasting; Probabilistic forecasting; Renewable energy sources
• ISSN: 2520-8942; 2520-8942
• DOI: 10.1186/s42162-022-00213-8

In the electricity grid, constantly balancing the supply and demand is critical for the network’s stability and any expected deviations require balancing efforts. This balancing becomes more challenging in future energy systems characterised by a high proportion of renewable generation due to the increased volatility of these renewables. In order to know when any balancing efforts are required, it is essential to predict the so-called net load, the difference between forecast energy demand and renewable supply. Although various forecasting approaches exist for both the individual components of the net load and the net load itself, it is unclear if it is more beneficial to aggregate several specialised forecasts to obtain the net load or to aggregate the input data to forecast the net load with one approach directly. Therefore, the present paper compares three net load forecasting approaches that exploit different levels of aggregation. We compare an aggregated strategy that directly forecasts the net load, a partially aggregated strategy that forecasts demand and supply separately, and a disaggregated strategy that forecasts demand and supply from each generator separately. We evaluate the forecast performance of all strategies with a simple and a complex forecasting model, both for deterministic and probabilistic forecasts, using one year of data from a simulated realistic future energy system characterised by a high share of renewable energy sources. We find that the partially aggregated strategy performs best, suggesting that a balance between specifically tailored forecasting models and aggregation is advantageous.