A historical weather forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) for energy forecasting

Abstract

Weather is often found to be a key driving factor for power generation and energy consumption. To capture the effects of weather, many energy forecasting practices, such as load forecasting, renewable power generation forecasting, gas and electricity price forecasting, and power distribution systems outage forecasting, would rely on numerical weather prediction (NWP). In the academic literature, however, energy forecasting models have often been developed based on settings of ex post forecasting, where the actual observations of weather variables during the forecasted period are being used. Such gap between academic research and field practices is partly due to the shortage of historical weather forecasts. To that end, an NWP forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) High Resolution (HRES) model, as available in the ECMWF’s Archive Catalogue, is offered to the energy forecasting community under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Since the raw data is massive in size, a subset which is thought sufficient for energy forecasting research purposes is provided through this article. Four years (2017–2020) of HRES forecasts of 14 frequently used weather variables, over the geographical region bounded by 63° N, −126°W, 21° S, and 36° E (most of Europe and North America), on a 0.5°by 0.5°longitude/latitude grid, are released in the form of NetCDF files. This dataset is able to support a variety of aforementioned energy forecasting tasks. In addition to introducing various means to utilize the dataset, this article provides a set of case studies on post-processing of day-ahead solar forecasts. The R code being used to produce the results shown in this article is also made available, so that the readers can reproduce this case study as well as adopt the code for other relevant studies.