A Composite Extended Nearest Neighbor Model for Day-Ahead Load Forecasting

Abstract

Day-ahead load forecasting is an important task for the reduction of electricity waste and efficient management of a smart grid. The electricity load profile data reveals the correlation of electricity load demand with weather condition, day type (working day or holiday), time of the day and season of the year. Thus the load forecasting problem has a high degree of complexity with consideration of those variables as input. To solve the problem of day-ahead short-term load forecasting (STLF), the proposed solution first classifies load profile data into different classes. To this end, a recent developed classification approach called extended nearest neighbor (ENN) algorithm is adopted. Then, a composite ENN model is proposed for day-ahead load forecasting. The composite ENN model consists of three individual ENN models which are combined together by tuned weight factors for predicting final forecasting output. Unlike other statistical and computational intelligence approaches, the composite ENN model predicts electricity load demand from the maximum gain of intra-class coherence. By exploiting intraclass coherence from the generalized class-wise statistic of all available training samples, the composite ENN algorithm is able to learn from global distribution and therefore improve the accuracy of load forecasting. The proposed method is validated on two case study: (i) Australian National Energy Market Data and (ii) Brookings, South Dakota, USA Data. For case study 1, mean absolute percent error (MAPE) of composite ENN based load forecasting is decreased by 44.68% compared to composite kNN based load forecasting and mean absolute error (MAE) is decreased by 45.52%. Similarly for case study 2, the decrease of MAPE and MAE values are 27.72% and 31.65% respectively.