Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case
Aurora Poggi, Luca Di Persio, Matthias Ehrhardt
Abstract
Our research involves analyzing the latest models used for electricity price forecasting, which include both traditional inferential statistical methods and newer deep learning techniques. Through our analysis of historical data and the use of multiple weekday dummies, we have proposed an innovative solution for forecasting electricity spot prices. This solution involves breaking down the spot price series into two components: a seasonal trend component and a stochastic component. By utilizing this approach, we are able to provide highly accurate predictions for all considered time frames.
Topics & Concepts
Spot contractElectricity price forecastingElectricityComponent (thermodynamics)Computer scienceEconometricsElectricity priceTime seriesGermanElectricity marketArtificial intelligenceMachine learningEconomicsEngineeringFinancial economicsFutures contractGeographyPhysicsElectrical engineeringThermodynamicsArchaeologyEnergy Load and Power ForecastingImage and Signal Denoising MethodsForecasting Techniques and Applications