Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach

Abstract

The paper presents a way of creating three new, innovative variables based on air temperature to be used in forecasts of electricity demand and prices. The forecasting methods developed so far, especially in the area of energy prices, either did not use temperature data or were based on data that had not undergone pre-processing, which made it difficult for the model to use their potential. Newly developed variables have a linear relationship with the demand for electricity. This paper describes in detail the procedure for determining the parameters of new variables using the electricity market in Poland (a country in Central Europe) as a case study. The proposed approach allows both to avoid data clustering into different seasons and to precisely determine the temperatures at which the nature of the dependence with the demand for electricity changes. The validity of the proposed new variables in prognostic models has been confirmed by their use in deep neural networks. The proposed approach allows reducing the sMAPE by up to 15.3%. The designed new explanatory variables can be used not only in models based on artificial intelligence tools, but also in other forecasting methods that allow the use of exogenous inputs.