Electricity Price Forecasting for Nord Pool Data

Abstract

In many countries deregulation of power markets was undertaken to create a more efficient market. As a result, electricity now can be purchased and sold across areas and countries more easily. For participants of electricity market it is beneficial to forecast future prices in order to optimize risks and profits as well as make future plans. A number of various methods is applied for solving this problem. However, the accuracy of forecasts is not sufficient as market spot price of electricity has features such as seasonality, spikes or high volatility. Furthermore, diverse approaches work differently with distinct countries (markets). In this paper we discuss our experiments with electricity spot price data of Lithuania's price zone in Nord Pool power market. Day-ahead forecasts are made using Seasonal Naïve, Exponential smoothing, Artificial Neural Networks.