Net load forecasting using different aggregation levels

Abstract

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.

Probabilistic Net Load Forecasting Framework for Application in Distributed Integrated Renewable Energy Systems

“…In order to optimize the use of renewable energy, it is necessary to align the generation of renewable energy sources and consumption, minimizing the residual or net loads. The capacity to deterministically predict the net load has been further developed in recent years [23,25,26].…”

“…In recent years, several approaches to predicting deterministic net load have been unveiled [40,41]. The probabilistic prediction of net load can be performed at various levels of aggregation [26]. When aggregating individual probabilistic predictions, the probability densities of two or more continuous random variables must be convolved as a joint probability density [42].…”

Using weather data in energy time series forecasting: the benefit of input data transformations

“…New advances in machine learning, for example, offer tools to better forecast renewable energy sources and demand given various weather input data. These approaches use weather data in different formats like single time series, e. g. (Dahl et al 2017;Hu et al 2021;Ren et al 2022;Elizabeth Michael et al 2022;Beichter et al 2022), to grid-based data, e. g. (Feng et al 2022;Kong et al 2020;Si et al 2021), or graphs, e. g. (Hu et al 2022;Simeunović et al 2022).…”

Direct Short-Term Net Load Forecasting Based on Machine Learning Principles for Solar-Integrated Microgrids

“…The forecasting models achieved MAPE values of 1.12% and 1.31% for direct and indirect forecasting, respectively. In a more recent study [33], the following three strategies were presented for STNLF: (a) aggregated, (b) partially aggregated, and (c) disaggregated. The results proved that the partially aggregated strategy exhibited the highest forecasting performance.…”