Forecasting Framework for Open Access Time Series in Energy
This provides a practical tool for energy forecasting in Europe using publicly available data, but it is incremental as it builds on existing methods with new data integration.
The authors tackled the problem of automated forecasting for energy-related time series using open access data from ENTSO-E, achieving comparable prediction accuracy to actual load data and country-provided estimates in many cases. They also extended the framework to include probabilistic forecasting and integrated it into a web-based API for easy access.
In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting - that is, providing a probability distribution rather than a simple point forecast - and incorporate it into a web based API that provides quick and easy access to reliable forecasts.