Probabilistic mid-term net load forecasting considering the effect of solar power using extreme learning machine

The increase in power generated by using solar energy increases the uncertainty in the power grid, since the majority of meters measure only the net load of a grid regardless of the output of distributed generations. This paper proposes a framework for probabilistic mid-term net load forecasting in a power grid based on separate forecasts of the load and output power of a solar station using the combination of principal components analysis and the extreme learning machine methods. The data used in this paper is related to the NERL and GEFCom2014 data bases and the matrix of scores is extracted by the use of principal component analysis. The prediction models are trained using ORELM model and evaluated in three sections: training, validation and mid-term prediction. The main objective of the proposed method is to increase the precision of net load forecasting by improving point forecasts. The comparison between the results presented in this thesis with other references shows that the MAPE error of predicted load, and predicted output power of the solar station improved  to 1.1333 and 0.3118, respectively, which will reduce overall forecast error.