Kourtis, GeorgeHadjipaschalis, IoannisPoullikkas, Andreas2016-03-012016-03-012011Kourtis, George, Ioannis Hadjipaschalis, and Andreas Poullikkas. "An overview of load demand and price forecasting methodologies." International Journal of Energy and Environment 2, no. 1 (2011): 123–150.2076-28952076-2909http://hdl.handle.net/11073/8167In this work, an overview of the various methodologies developed in recent years for short, mid and long term load and price forecasting is carried out. In the analysis the advantages and disadvantages of each method are introduced, together with the factors that influencing the different types of forecasting. Unless the effects of these factors are well taken into consideration errors can occur in the forecasting results and that results in increasing operational costs. The analysis indicates that the best suited method for all types of forecasting is artificial neural network, which outperforms better with nonlinear functions and on weekend days or national holidays. If are not to be distinguished from week day data, weekend and national holidays data a good alternative would be an autoregressive integrated moving average based method.en-USload forecastingprice forecastingunit commitmentartificial neural networksArticle