Electric Power Systems Research 148 (2017) 35–47 Contents lists available at ScienceDirect Electric Power Systems Research j o ur na l ho mepage: www.elsevier.com/locate/epsr Online electricity demand forecasting based on an effective forecast combination methodology Abderrezak Laouafi a,∗ , Mourad Mordjaoui a , Salim Haddad b , Taqiy Eddine Boukelia c , Abderahmane Ganouche a a Department of Electrical Engineering, University of 20 August 1955-Skikda, Skikda, Algeria b Department of Mechanical Engineering, University of 20 August 1955-Skikda, Skikda, Algeria c Department of Mechanical Engineering, University of Constantine 1, Constantine, Algeria a r t i c l e i n f o Article history: Received 8 May 2016 Received in revised form 17 February 2017 Accepted 19 March 2017 Keywords: Online load forecasting Forecast combination Hampel filter Anomalous load a b s t r a c t This paper presents a new forecast combination methodology for generating very short-term electricity demand predictions under both normal and anomalous load conditions. The main contribution of the work is to propose an online load forecasting system that has the ability to achieve good forecasting accuracy, avoid large forecasting errors and ensure low computation time. The real-time load data from the French power system and the Australian dataset for the state of New South Wales are used as an illustrative example to evaluate the performance of the proposed methodology. The results reflect that the developed approach has better forecasting performance than other methods considered in this study. For example, the results from the public holidays in France showed an average mean absolute percentage error (MAPE) of 0.863%, and the accuracy improvements over a simple average combination method, the best individual method, and a weighted combination are 15.887%, 13.353%, and 3.034%, respectively. For the case of the Australian load dataset, our forecasting system achieved an average MAPE of 0.860% and the improvement in comparison to a benchmark algorithm from the literature is equal to 8.316%. © 2017 Elsevier B.V. All rights reserved. 1. Introduction Electricity demand forecasting is the primary prerequisite for achieving the goal of sustainable energy management and eco- nomic and secure operation of modern power systems. The task of knowing the electricity demand in advance is needed to sustain supply/demand balance and to manage the process of electricity production, distribution and consumption on a variety of temporal scales: very short-term from few minutes to an hour, short- term from an hour to one week [1–5], medium-term from one week to one year [6–9], and long-term from one year to decades [10–12]. In particular, the accurate very short-term load fore- casts (VSTLF) are needed for the real-time scheduling of electricity generation as well as load-frequency control and economic dis- patch functions. With the deregulation of electricity markets and the growing penetration of renewable energy sources into the energy matrix of today’s power networks, such predictions are also of importance to market participants to mitigate the effects ∗ Corresponding author. E-mail address: a.laouafi@univ-skikda.dz (A. Laouafi). of renewable energy sources intermittence on grid stability and reliability. Over the past few years, researchers have studied this prob- lem of improving VSTLF accuracy and various classical and artificial intelligence techniques have been considered using different sim- ulated datasets. Classical methods include Box–Jenkins time series models [13,14], exponential smoothing [15,16], and Kalman fil- tering [17,18]. Artificial intelligence techniques include neural network [19–21], fuzzy logic [13], adaptive neuro-fuzzy inference system [22,23], and support vector regression [24]. Hybrid methods can be also used to combine the advantages of several algorithms. A typical way to create a hybrid model is to use the empirical mode decomposition as a tool for decomposing the electric load time series into a limited number of intrinsic mode function compo- nents, and to forecast the decomposed sequences with appropriate computational intelligence models. However, the application of empirical mode decomposition requires a significant execution time and the forecaster may face difficulty to handle the mode- mixing problem, since the components of new time series may significantly differ from those used while training the forecasting models [25]. In the other hand, wavelet decomposition is an effi- cient technique to unfold the inner features of the electric load time series. In this manner, many recent papers discussed the http://dx.doi.org/10.1016/j.epsr.2017.03.016 0378-7796/© 2017 Elsevier B.V. All rights reserved.