Forecasting Framework for Open Access Time Series in Energy

TL;DR

This paper introduces an automated framework for forecasting energy time series using open access data from ENTSO-E, capable of providing accurate, timely, and probabilistic forecasts via a web API for European countries.

Contribution

The paper presents a novel open access forecasting framework that leverages publicly available data and includes probabilistic forecasting integrated into a user-friendly web API.

Findings

Forecasts are timely and have comparable accuracy to existing estimates.

The probabilistic forecasting approach enhances the reliability of predictions.

The framework is applicable across multiple European countries.

Abstract

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.