Long term electric load forecasting based on particle swarm optimization M.R. AlRashidi * , K.M. EL-Naggar Electrical Engineering Department, College of Technological Studies, Shuwaikh, Kuwait article info Article history: Received 19 February 2009 Received in revised form 2 April 2009 Accepted 16 April 2009 Available online 19 May 2009 Keywords: Forecasting Particle swarm optimization Least error Peak load Estimation abstract This paper presents a new method for annual peak load forecasting in electrical power systems. The prob- lem is formulated as an estimation problem and presented in state space form. A particle swarm optimi- zation is employed to minimize the error associated with the estimated model parameters. Actual recorded data from Kuwaiti and Egyptian networks are used to perform this study. Results are reported and compared to those obtained using the well known least error squares estimation technique. The per- formance of the proposed method is examined and evaluated. Finally, estimated model parameters are used in forecasting the annual peak demands of Kuwait network. Ó 2009 Elsevier Ltd. All rights reserved. 1. Introduction One of the primary tasks of an electric utility is to accurately predict load requirements at all times. Results obtained from load forecasting process are used in planning and operation. For exam- ple, long-term load forecasting, one to ten years ahead monthly and yearly values, is applied in expansion planning, inter-tie tariff setting and long-term capital investment return problems. While short-term load forecast results, one day to one month ahead hourly and daily values, are needed in unit commitment, mainte- nance and economic dispatch problems. Therefore the accuracy of load forecasting has significant effect on power system planning and operation. The time horizon for mid and long-term forecasting ranges between few weeks and several years. Unfortunately, it is quite difficult to forecast load demand over a planning period of this length. This fact is due to the uncertain nature of the forecast- ing process. There are large numbers of influential factors that characterize and directly or indirectly affect the underlying fore- casting process; most of them are uncertain and uncontrollable. Many classic approaches have been proposed and applied to long-term load forecasting to estimate model parameters, includ- ing static and dynamic state estimation techniques [1–4]. While the least error square (LES) technique has been the most famous conventional static estimation technique and in use for a long time as the preferred technique for optimum estimation in general, some limitation and disadvantages are associated with this approach. For example, when the data set is contaminated with bad measurements, the estimates may be inaccurate unless a large number of data points are used. Ref. [5] proposed a static method based on noniterative least absolute value technique. This method has the advantage of detecting bad data. Another powerful class of estimation is the stochastic dynamic one. Kalman filtering and the least absolute value filtering algo- rithms are examples of such dynamic approaches. Unlike static ap- proaches, where the whole set of data is used to obtain the optimal solution, dynamic filters are recursive algorithms. In recursive fil- ters, the estimates are updated using each new measurement. Dy- namic filters are well suited to on-line digital processing as data are processed recursively. They had been used extensively in esti- mation problems for dynamic systems [6]. Dynamic filters have the advantage of being able to handle measurements that change with time. Methods based on artificial intelligence such as artificial neu- ral networks (ANN) and expert systems have been also proposed and shown promising and encouraging results [7,8]. Support vector machine (SVM) has been an attractive tool for load forecasting re- cently. SVM is a form of machine learning method which is devel- oped from statistical learning theory. Like ANN, the SVM has the problem of network parameter selection [9]. Heuristic search methods like genetic algorithms (GA) were also proposed and implemented. This method is based on the mechanism of natural selection and natural genetics [10]. Hybrid methods using ANN, GA, SVM were also proposed in many Refs. [11,12]. This paper presents a new method for long-term load forecast- ing using particle swarm optimization (PSO) technique. PSO is a global optimization algorithm that deals with problems in which a best solution can be represented as a point or surface in 0306-2619/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2009.04.024 * Corresponding author. Tel.: +965 2231 4312; fax: +965 24816568. E-mail addresses: malrash2002@yahoo.com (M.R. AlRashidi), knaggar60@ hotmail.com (K.M. EL-Naggar). Applied Energy 87 (2010) 320–326 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy