Long Term Load Forecasting Using SA-ANN Model: a Comparative Analysis on Real Case Khorasan Regional Load

Transcription

1 No. E-13-AAA-0000 Long Term Load Forecasting Using SA-ANN Model: a Comparative Analysis on Real Case Khorasan Regional Load Rasool Heydari Electrical Department Faculty of Engineering Sadjad Institute of Higher Education Mashhad, Iran Rasool.heydari@gmail.com Ali Ramazani Electrical Department Faculty of Engineering Sadjad Institute of Higher Education Mashhad, Iran Mostafa Rajabi Mashhadi Khorasan Regional Electrical Company Mashhad, Iran Mrajabim@yahoo.com Abstract long term electric power system load forecasting (LTLF) plays an important role in Energy Management System (EMS), which has remarkable influence on planning, control system, generation expansion and economic issues on power systems. While several promising techniques have been done in the short-term load forecasting, no trustworthy methods have been contemplated for long-term predictions. The intent of this paper is to introduce two approaches based on the regression method and Artificial Neural Network (ANN) with considering on input data for prediction of real case Khorasan regional load. Furthermore, we apply combination of Simulated Annealing and Artificial Neural Network (SA-ANN) in order to increasing accuracy in LTLF. Comparison of the results illustrate that proposed method have a reliable solution for long term load forecasting of Khorasan, Iran and in more than 95% of test result, SA-ANN give better solutions than ANN and AR methods. Keywords Power System Planning, Long Term Load Forecasting (LTLF), Regression Methods, Artificial Neural Network (ANN), Simulated Annealing ANN. I. INTRODUCTION LONG Term electric Load Forecasting (LTLF) is the first and L the most important step of power system planning which is on the time horizon of one to ten years. The natures of short term and long term forecasts are quite different. LTLF is a complex kind of problems. Among other factors, accuracy of LTLF is considerably influenced by unpredictable factors like weather as well as social behaviour of the community of that load, politician and government policies and etc. These factors are rarely predictable for long term load forecasting time horizon of several years. Conversely, short term load forecasting, though affected by climate condition and daily social habits, the weather and social habits fluctuation for the short term time horizon of hours is small enough to predict load with high accuracy. Base on the reports in [1] some short term forecasting algorithms have results with mean absolute error of less than 1%. Due to deregulation energy market, researchers and planner focused on the saving and the optimization of power management. In this framework, in order to minimize the cost of operation and investment on generation expansion and develop infrastructure, especially for micro grids and smart grid industry, an accurate long term load forecasting plays an important role [2]. During the last two decades many forecasting methods have been developed to improve the long term energy load forecasting accuracy. The traditional method of LTLF was based on the regression methods. In [3] Auto Regressive (AR) and Support Vector Regression (SVR) are described, and both of them were tested in the period of six month to a year as mid-long term load forecasting. The time series models thorough Auto Regressive (AR), Moving Average (MA), and the Auto Regressive Moving Average (ARMA) are widely discussed at present [4], [5] and [6]. The most important require of these methods are massive amount of historical load data in order to produce optimal models and reliable forecasting results. Kandil and El-Debeiky in [7] classified forecasting methods according to the forecasting problem using a knowledge-based expert system (ES) and tested it for peak load in fast and normal developing power systems. Furthermore, the expert system is very flexible in updating the forecasting methods and heuristic rules, it is expected that the developed expert system can be served as a valuable assistant to system planners in performing their annual load forecasting duties. Gray predictions systems are used in [8]. Gray theory is appropriate for load forecasting, because its principle is simple

2 and calculation is rational in small samples. But there are many limitations. New Gray model described in [9] developed Gray theory and consequences are compared. Combination forecasting model for medium term load forecasting based on least squares support vector machines (LS-SVM) and evolutionary algorithms like particle swarm optimization (PSO) algorithm is presented in [9], moreover result are collated and shown that accuracy improved in MPSO-LS-SVM simulation model. Also particle swarm optimization is used again for combination with Fuzzy Neural Network in [10] and applied well to a real case in Taiwan campus. In recent years, the attention of researchers and planners have been attracted by artificial intelligent methods in application to load forecasting, such as expert systems [11], neural network (NN) [12], [13] and fuzzy logic system [14], [15], [16]. Density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful and necessary for long term planning. In [17] density of long-term peak electricity demand, including modelling, simulating and forecasting is discussed widely. The intention of this paper is to present a comprehensive model for long term load forecasting. Most LTLF methods use one of the actuarial techniques or artificial intelligence algorithms such as regression, neural networks and fuzzy logic and expert systems. We apply the Regression and ANN methods as two possible LTLF options to forecast daily platform of loads. Then we add PSO to ANN to improve forecast result. Finally proposed method of LTLF will be tested in Khorasan, load. II. LONG TERM LOAD FORECASTING First of all, finding an authentic LTLF method necessitates a good knowledge on kinds of loads and available forecasting techniques for modelling a particular type of load. Since different types of load have different response to corresponding load factors and exhibit different growth or decline pattern, loads go to three main groups: residential, commercial and industrial [18]. Residential and commercial loads are depend on the weather changes, because weather sensitive devices such as heater and air conditioner are used widely in these categories, so there are seasonal fluctuation in residential and commercial loads. On the other hand, for industrial loads, annual growth rate is most uniform and depend on type of industry, these loads because of shift operation have unique characteristics. Secondary, many factors have an impact on LTLF, as mentioned before; most of these factors are rarely predictable. For instance, Regional development (geographic and location) is not predictable factor. Some of the most important factors that dramatically effect on LTLF are: Historical load data Population and customers Energy supply and price Fuel price GDP growth Government policies Historical weather data Economic Among these factor, population growth rate, weather and regional temperature data play an important role in reliable LTLF. Economic Condition Load Data Long Term Load Forecasting Populatin And Costomer Energy Price Fig. 1. LTLF and corresponding load factors Weathe r Data GDP Growth Fuel Price III. TIME SERIES METHOD FOR LTLF Time series is a complex of observations according to time which have been serialized as follows: XX tt1,xx tt2,,xx ttnn A time series analysis can be sorted as several sections which will be explained follow: a) Statement: The first step of time series analysis is usually draw a simple graph of data and measures in period of time. b) Explain: When observations are categorized to one or more variables it is second step to changes of a time series to the other series. In this section some regression methods can be helpful. c) Forecasting: A time series is used to predict future amount of the series data. This section is the most important part of time series analysis. d) Fitting pattern: in fact fitting stage, determinate the proper model based on the lowest error or other parameters. Time series Model Types are: autoregressive models, Moving average model and mixed model. In some cases, combination models of autoregressive -Moving average and autoregressive - moving average accumulative are used. In figure 2 time series of Khorasan Load regional are shown. A. Auto Regressive (AR) model First of all, time series regression models are used for LTLF. Generally, regression methods attempt to forecast variations in some variable of interest. Mathematical model is: Y t = ϕ 1 y t-1 + ϕ 2 y t-2 + ϕ 3 y t ϕ n y t-n +q (1) Where Y t is the dependent variable, y t-1 y t-n are explanatory variables correlated with Y t, q is a random variable with zero mean and constant variance and ϕ 1 ϕ n are regression coefficients which are determined by least square error technique. B. Data description and analysis The forecasts presented in this paper are for Khorasan, iran. Khorasan is largest state of Iran with over 6 million populations. 2

3 Load data were obtained from Khorasan Regional Electrical Company from March, 2006 to March Time plots of the daily loads data of Khorasan are illustrated in Figs. 1, which clearly show the day pattern from 2006 to In general, historical information (load data, weather data, population data, etc.) are always required for a load forecasting model. The more historical data will lead to more accurate LTLF. that the rate of variations is smooth, so regression method is suitable for it. Fig. 4. Peak load of khorasan, Iran The regression model does not lead to good LTLF results because it uses linear combinations of variables and, therefore, is not adapted to modelling grossly nonlinear complex interactions as shown in this case. When such method is applied to a fast changing system, with sudden changes in load, or a system with uncertain conditions, forecasting results will deteriorate. Fig. 2. Daily Load of Khorasan, Iran In this section, a load forecasting model via only historical load data is designed to simplify the forecasting structure of auto regressive method. The long term load forecasting (LTLF) model is designed to forecast the daily load at the next two years. Fig. 5. Real data and Forecasted Data of peak load of Khorasan, Iran Figure 5 shows forecasted data and real data of peak load of khorasan, iran during 2001 to It can be say that forecasted data are suitable for peak load of khorasan state becauce flactuations of peak load are smooth. Fig. 3. Regression method for LTLF As it can be clearly seen from the figure 3, auto regressive method has non negligible error in high fluctuations, but this method are used in general. Red line shows actual data and blue one shows forecasted data. It is clear from the data given that in the last days of second year, forecasted data have remarkable error with actual data. On the other hand, Peak load not dramatic fluctuation during period of time, so expected that regression method is suitable for Long term Peak load forecasting. Figure 4 demonstrate peak load growth rate of Khorasan, Iran from 1970 to Based on the graph, it can be stated IV. ARTIFICIAL NEURAL NETWORK Recent years, by developing artificial intelligent methods Artificial Neural Network (ANN) get appropriate for modelling nonlinear dynamic systems which is the Recurrent Multilayer Perceptron (RMLP). The ANN technology is attempting to emulate the information processing methods of live neural system. Attractive feature of the RMLP is the explicit distinction among its layers, visually simplifying the separation of the feed forward from the feedback part of the network. The most important part of designing a good ANN structure is choosing appropriate input variables. In the proposed ANN, neurons in the input layer including: 3

4 Historical Load data Load data of same days of previous years Load data of same day of previous week Load data of same day of previous month The average of load data of 3 days later The average of load data of previous month Previous monthly peak load data Previous annual peak load data The relationship between load and average temperature is shown in Figure 7, where a nonlinear relationship between load and temperature can be clearly seen. Use of RMLP architecture as a load forecasting tool involves pre-processing of the relevant historical data and training for estimation of the network weight. In this paper, three layers perceptron and 20 neurons are used for prediction of next two years load. Historical temperature data Temperature data of same days of previous years The average of temperature data of previous week The average of forecasted data of next week Population growth rate Weather data were obtained from weather center of Khorasan from March, 2006 to March Fig. 8. Actual data and trained data in ANN Fig. 6. Historical Temperature data As mentioned before, weather plays an important role in load changes; in Figure 6 Time plots of the daily temperature data is shown. Obviously, the load pattern in Figure 2 has much similarity with temperature pattern in Figure 6 that show residential and commercial load are the main load of Khorasan. Fig. 9. Actual data and tasted data in ANN Fig. 10. Normalized Actual data and trained data Fig. 7. Historical temperature vs. load data Base on the Figure 8, Figure 9 and Figure 10, ANN system trained and tasted well. As it can be seen from Figure 8 actual data and trained data has a same trend which shows ANN system could trained data at the best. Figure 8 shows normalized tasted data and actual data and it can be illustrated that tasted data coincident with actual data with negligible 4

5 error that shows ANN system tasted data well. Furthermore actual data and trained data in Figure10 focused on the line y=x that shows after 1000 epoch iteration, ANN system trained and have a reliable result. The mean squared error (MSE) of this trained network is and for tasted data is that demonstrate high accuracy and suitable performance for ANN system. Figure 12 Shows the Actual data and forecasted data of LTLF by using SA-ANN method. Obviously, forecasted data (red line) follow actual data (blue line) in the best accuracy. By using SA for filtering input data the mean squared error (MSE) of this trained network decreased to and for tasted data decline to that illustrate accuracy increased sharply and this network has suitable performance. V. SIMULATED ANNEALING-ANN Simulated annealing is a probabilistic evolutionary algorithm proposed in Cerny (1985) for finding the global minimum of a cast function that may possess several local minimum. It works be emulating the physical process whereby a solid is slowly cooled so that eventually its structure is frozen, this happens at a minimum energy configuration. Table 1. Mean squared error of ANN and SA-ANN Algorithms Mean Squared Error Algorithm Trained Data Tested Data ANN SA-ANN Fig. 11. SA-ANN algorithm VI. DATA ANALYSIS In pervious sections, two most reliable method of LTLF that are more popular for planner and researchers are described. Furthermore a new method of forecasting by combination of simulated annealing algorithm with artificial neural network is discussed. Now, in order to compare the results of these three methods, we used SPSS software. SPSS Statistics is a software package used for statistical analysis. According to the results of LTLF from pervious sections and comparing with SPSS, the most reliable result is for SA-ANN. The main objective of the forecasting structure is to forecast the load change rate. In SA-ANN method we tried to filter input data of ANN by SA algorithm in order to find more accurate result. The cost function of SA is residuum of change rate at trained data and actual data and finally by calculating error in output data of ANN, the Ineligible input data will removed and final network will trained. In this algorithm, not only accuracy increased but also speed of ANN system response increased. Fig. 13. Output SPSS comparison Fig. 12. SA-ANN method of LTLF Based on the SPSS comparison result that use Paired Sample Sign Test, in more than 95% of test result, SA-ANN give better solutions than ANN and AR. 5

6 For iterations we have: REFERENCES Fig. 14. SPSS comparison for iterations VII. CONCLUSION In this paper three models were proposed for LTLF. The first one is based on regression method. Auto regressive model is used for LTLF. The second one is based on ANN. To illustrate the performance of proposed model, real data from Khorasan, Iran Loads was used. The regression wasn t successful in accurate forecast. Results showed that ANN had a better performance in LTLF because of its flexibility in handling nonlinear systems, superb performance in dealing with noisy or incomplete data, and ability to learn from historical data and generalize the solution based on input data. Furthermore temperature of Khorasan that effect on LTLF and annual population growth rate determined as input data for ANN. On the other hand, by using simulated annealing algorithm, input data are optimized for ANN and MSE decreased for tested and trained data from and to and respectively. Finally, result of LTLF for next two years of these three methods are compared with SPSS software and the results indicated that the proposed model of SA-ANN has more efficiency to forecast future load for next several years and in more than 95% of test result, SA- ANN give better solutions than ANN and AR methods. [1] H. Hippert, C. Pedreira, Neural networks for short term load forecasting: a review and evaluation, IEEE Trans.Power syst., Vol. 16, No.1, pp.44 55, [2] G. W. Arnold, Challenges and opportunities in smart grid: A position article, Proceedings of the IEEE, vol.99, no. 6, pp , [3] D. Anguita, L. Ghelardoni, A. Ghio, Long term energy load forecasting using auto regressive and approximating support vector regression, 2 nd IEEE energycon conference & exhibition, 2012 [4] E.H. Barakat, S.A. Al-Rashid, Long-term peak demand forecasting under conditions of high growth, IEEE Trans. Power Systems, Vol. 7(4), 1992, [5] G. Cai, D. Yang, Y. Jiao, Ch. Pan, The characteristic analysis and forecasting of mid-long term load based on spatial auto regressive model, International Conference on Sustainable Power Generation and Supply, [6] M. Shahidehpour, H. Yamin, and Z. Li, Market Operations in Electric Power Systems, New York, John Wiley & Sons, [7] M. S. Kandil, S. M. El-Debeiky, N. E. Long term load forecasting for fast developing utility using knowledge based expert system IEEE Trans. Power system, VOL. 17, NO. 2, MAY 2002, [8] W. Zhao, D. Niu, A mid-long term load forecasting model based on improved grey theory, The 2nd IEEE International Conference on Information Management and Engineering (ICIME), 2010, [9] D. Niu, H. Lv; Y. Zhang, Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm, International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS ' , [10] R. Jongwai, Y.C. Huang, Y.Ch. Chen, Design of intelligent long term load forecasting with fuzzy neural network and particle swarm optimation, Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, July, [11] N. Jun, Y. Qiyu, The Application of Expert System in Power System Load Forecasting, Hunan Electric Power, Vol. 6, pp. 8-11, 1994 [12] M. Ghiassi, D. K. Zimbra, H. Saidane, "Medium term system load forecasting with a dynamic artificial neural network model" IEEE Elect. Power Syst. Res., vol. 76, no.5, pp , [13] A.G. Parlos, A.D. Patton, Long term load forecasting using a dynamic neural network architecture, Planning operation and control of today electric power system, Athens, Greece, 1993 [14] N.E. Charalambos, D. H. Nikos, "An annual midterm energy forecasting model using fuzzy logic, " IEEE Trans. Power Syst., vol. 24, no. 1, pp , 2009 [15] W. Biao, F. Gang, H. Jing, X. Yong, Electric power load forecasting based on fuzzy optimal theory International conference on intelligent system design and engineering application, 2012 [16] H. Daneshi, M. Shahidehpour, A. L. Choobari, Long term load forecasting in electricity market, IEEE Trans. Power syst., Vol. 24, pp , 2008 [17] R.J. Hyndman, Sh. Fan, Density forecasting for long term peak electricity demand, IEEE Trans. Power Syst., Vol. 25, No.2, pp , 2010 [18] T.E. huayllas, D.S. Ramos, Electric power forecasting methodologies of some south American countries: a comparative analysis, IEEE latin America Trans. Vol. 8, No. 5, pp , 2010M. 6