07583810 day ahead price forecasting in deregulated electricity market using artificial neural network

Now a days the price forecasting plays a very essential role in a new electricity industry; it helps the independent generators to set up optimal bidding patterns and also for designing the physical bilateral contracts. In general, different market players need to know future electricity prices as their profitability depends on them. There are many papers have been presented on the forecasting of electricity market price such methods are based on time series, artificial intelligence and hybrid methods. In this paper, the price forecasting is presented by using feed forward artificial neural network by using historical price data. Accurately and efficiently forecasting of electricity price is more important. Therefore in this paper, an Artificial Neural Network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market. The proposed ANN model is a four layered neural network, which consists of, input layer, two hidden layers and output layer. Matlab is used for training the proposed ANN model. Electricity load and wind forecasting can also be done using this method which helps in planning and operation of the power system.

Day Ahead Price Forecasting in Deregulated Electricity Market Using Artificial Neural Network Ms Kanchan K Nargale Mrs S B Patil Department of Electrical Engineering, G H Raisoni Institute of Engineering & Technology, Wagholi, Pune, India nargale.k@gmail.com Department of Electrical Engineering, G H Raisoni Institute of Engineering & Technology, Wagholi, Pune, India sangita.patil@raisoni.net Abstract— Now a days the price forecasting plays a very essential role in a new electricity industry; it helps the independent generators to set up optimal bidding patterns and also for designing the physical bilateral contracts In general, different market players need to know future electricity prices as their profitability depends on them There are many papers have been presented on the forecasting of electricity market price such methods are based on time series, artificial intelligence and hybrid methods In this paper, the price forecasting is presented by using feed forward artificial neural network by using historical price data Accurately and efficiently forecasting of electricity price is more important Therefore in this paper, an Artificial Neural Network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market The proposed ANN model is a fourlayered neural network, which consists of, input layer, two hidden layers and output layer Matlab is used for training the proposed ANN model Electricity load and wind forecasting can also be done using this method which helps in planning and operation of the power system Keywords— Electricity Market and Price Forecasting and Artificial Neural Network (ANN) I INTRODUCTION In a power market the price of electricity has important for all activities But in many countries the electricity industry has very low competitive energy and has less regulated power The price forecasting helps to the different power suppliers to sells the rational offers in short term The price forecasting helps to the electricity industries for the investment decisions and bidding strategies It is necessary for estimating the uncertainty involved in the price There are many methods are presents till now for the forecasting of electricity market price These methods are based on the artificial intelligence and time series In some forecasting of electricity market price uses both artificial intelligence and time series methods The price forecasting plays an important role in electricity industries; it helps to an independent generator to set the optimal bidding patterns and physical bilateral contracts [1] Generally the different electricity industries have needed to know the future electricity prices and the profitability depends on them Electricity price forecasting is very important to study because the electricity power market and electricity prices are 978-1-4673-9925-8/16/$31.00 ©2016 IEEE highly volatile in nature As the degree of volatility of electricity markets is higher than that of other markets, due to the risk of volatility is created in every market [2] Also the storage of electricity is very costly therefore the electrical supply and demand needs to be balanced in real time To balance the supply and demand properly many numbers of factors are to be considered such as production of hydro generation, generating units availability, effects of weather, changes to prices of related commodities such as fuel price, and sudden occurred physical problems in transmission systems and generation Generally for forecasting purpose there are different types of forecasting models are used like as traditional time series models in [2], Auto Regressive Integrated Moving Average (ARIMA) models, simpler Auto Regressive (AR) models modern techniques such as ANN, Fuzzy logic [3]have been used for price forecasting The traditional price forecasting models are uses the mathematical model for the regression analysis and time series analysis Also there are many artificial intelligent methods are used for price forecasting recently Out of these all methods the ANN method is very powerful tool and simple for price forecasting The ANN method is used in this paper to forecast the price because this method has high capability to learn the complicated relationship between the input and output through a supervised training process with historical data There are many factors are affected on the electricity price forecasting, these factors are line limit, load pattern, bidding pattern and generator outage Out of these factors the load pattern is the more effective parameter for bidding behavior of Generating Companies (Gencos) Therefore in this paper the historical price and load patterns are considered to forecast the price The three layered feed forward ANN method is used to shows the price forecasting results The Historical data for this market is collected from 2008 to 2010 in 24 hours Organization of this paper is in the following way section II reviews the development of system, the different proposed methods used in this paper are presents in this section In section III the Artificial Neural Network (ANN) Models are presents In section IV simulation of the proposed system and the experimental results are presents The simulation is done in MATLAB software Finally section V concludes this paper 527 II DEVELOPMENT OF SYSTEM This section reports the development of the proposed method this algorithm has been tested on training data set; also in this section the different modules are considered for designing the a good neural network model for short term price forecasting Figure indicates the simple flow diagram of the work done in price forecasting methodology A Collection of Data: The real time data Market clearing price (MCP) and Market clearing volume is taken from Indian Energy Exchange, Delhi (IEX) and Power Exchange India Limited, Mumbai (PXIL) [4].To find the optimal input parameters ANN uses the input selection from the collected historical data By using these optimal inputs ANN shows the more accurate result and has great speed Parameters, which effect on the electricity price can be categorized into day type (the day of a week), historical price data and the amount of demand (system load) The correlation analysis is used to predict the price of previous hours In this paper work the data is collected from 2008 to 2010 in 24 hours Out of this data collection 70% data is used for training the sequence and remaining 30 % data is used for validation purpose B Analyzing the Data: These MCP and MCV are analyzed From analysis it is found that price is volatile in nature and this volatility is higher than any other commodity The analysis of data is done by using normalization technique The formula for the normalization method is given by, (1) Collection of data (MCV & MCP) from IEX & PXIL Analysis of data by using Normalization technique Developing price forecasting model using ANN Price Estimation Analyzing the Results Fig.1 Proposed Block Diagram The normalization technique which is used in this paper has main advantage of mapping the target output to the nonsaturated sector of tensing function This technique is useful to improve the accuracy of both the forecasting modes and training data sequences C Developing the ANN model for price forecasting The forecasting model will be developed using artificial intelligence tool And this model will trained using the analyzed data D Training and Validation: The training process of ANN requires a proper network inputs and target outputs for forecasting the prices The training process the set of examples of data is given to the ANN network In the training process the biases and the weights of an ANN are properly adjusted to minimize the performance of network function In this method, historical price data has been used for forecasting the price in day ahead market Out of the total data collected, 70% of the data is used for training the sequence and remaining 30 % data is used for validation purpose E Result Analysis: Result will be analyzed by comparing the actual results and predicted results, the model can be tested for its efficient prediction III THE ARTIFICIAL NEURAL NETWORK (ANN) MODELS The Artificial Neural Network (ANN) based models is the first technique which is used for the price prediction This method has the most popular tool for different price load forecasting applications A The SVM Models The Support Vector Machine (SVM) models are one of the latest techniques which are used for electricity price forecasting Most of the recent techniques cannot handle the nonlinear price forecasting problems properly But in case of SVM, it shows the better performance than these methods The SVM model uses the statistical learning of theory which is used to minimize the structural risk, instead of the usual empirical risk of forecasting and for this purpose it minimizes the upper bound generalization error The SVM models are also used for solving problems of small sample size, classification, and regression and time series predictions of forecasting B Use of ANN for Price Estimation The artificial neural network (ANN) is a mathematical model which is based on biological neural network The artificial neural network (ANN) refers to the inter–connections between the neurons in different layers of each system This system has three layers, the first layer consists of input neurons which are useful to send data through synapses to the second layer of neurons, and after that more synapses to the third layer of the output neurons The ANN contains a group of artificial neurons which are interconnected to each other’s The ANN is 528 a nonlinear mathematical modeling tool which can be used for the complicated relationships between the inputs and outputs The figure shows the basic diagram of Artificial Neural Network (ANN) C Single layer feed forward networks: The neurons are organized in the layers in the layered neural network The layered neural network is the simplest form of neural network in which the input layer of source nodes are projects on to the output layer of neurons and vice versa and this process is called the feed forward network As shown in figure there are four input layer nodes and four hidden layer nodes are presents for both input and output layers And such network is called the single layered network [6]-[8] D Multilayer feed forward networks: The second class of a feed forward neural network distinguishes itself by the presence of one or more hidden layers, whose computation nodes are correspondingly called hidden neurons or hidden units The hidden neurons presents in the network creates the communication between the external input and the network output in some useful manner If the one or more hidden layers are added to the network, then the network is able to extract higher –order statistics Due to the extra dimension of neural network and extra set of connections, the network acquires a global perspective despite its local connectivity The main ability of hidden neuron is to extract the higher order mathematics from the input layer Figure shows the feed forward network with one hidden layer and one output neuron The source node presents at the input in the figure are used to get the input signals which are applied to the neurons In the second stage the hidden neurons are used to establish the communication between the input and the output The outputs obtained from this hidden layer are acts as input to the third layer and rest of the network [9] In the third layer that is the output layer the network constitutes the overall response of the network as shown in figure Fig.3.TheFeed Forward Network With One Hidden Layer and One Output Neuron E Development of ANN for Price Estimation • Collection of Data: Prices of January 2009 to April 2010 are collected • Selection of Input & Output: 24 MCP of each hour on a day before and 24 MCP of each hour of same day before a week give 48 MCP as an input to neural network And 24 MCP will be in output layer • Total Data: Total samples utilized for training and validation purpose are 455 • Training and Validation Data: Samples from January 2009 to March 2010 are used To train the data of network 70% sample and for test the data 30% samples are used April 2010 month sample is used for revaluation of network by comparing estimated price to actual prices A program is written in Matlab software for training of neural network IV EXPERIMENTAL RESULTS Fig.2 Basic Diagram of Artificial Neural Network This section shows the experimental results of proposed Price Forecasting in Day Ahead Market by using Artificial Neural Network (ANN) The training data is to be taken firstly The training is started with one hidden node and it is increased one by one increasing the performance of network At 218 Epochs the performance goal is met Total time required to train the network was 32 Minutes The optimum structure of neural network is 48-1024 Figure shows performance of neural network during training The training data is further proceeding to the ANN model to estimate the price Figure shows ANN model for trained 529 prices estimation By using this model future prices are estimated for unknown samples The samples which are not used in the training or testing are then evaluated for the performance of neural network Hourly prices are predicted for six days: 13th of April to 18th of April 2010 and then compared with the actual prices of these samples To evaluate the performance of an Artificial Neural Network (ANN) module, we compare its estimated price with those actual prices The Mean Absolute Percentage Error (MAPE) and Absolute percentage error (APE) is calculated using following formula Let Pa be the actual price and Pf be the forecast price Then, Absolute percentage error (APE) is defined as, (2) And MAPE is given by, (3) where N= time block Fig.4 Performance Graph Fig.5 ANN model obtained after Training Fig 6.Actual and Estimated Price for 13th April 2010 The comparison between actual and calculated price values for 13th April 2010 is shown in figure From this figure it is observed that minimum Absolute Percentage Error (APE) 0.820077 and maximum Absolute Percentage Error (APE) is 31.89093 The mean APE (MAPE) is 18.6161 Comparison between actual and estimated price values for 14th April 2010 shown in figure It is observed that minimum Absolute Percentage Error (APE) is 1.047389 and maximum Absolute Percentage Error (APE) is 33.36022 The mean APE (MAPE) is 14.35968 Fig 8.Actual and Estimated Price for 14th April 2010 Comparison between actual and estimated price values for 15th April 2010 shown in figure It is observed that minimum Absolute Percentage Error (APE) is 1.040385 and maximum Absolute Percentage Error (APE) is 34.3546 The mean APE (MAPE) is 15.74521 Comparison between actual and estimated price values for 16th April 2010 shown in figure 10 It is observed that minimum Absolute Percentage Error (APE) is 0.626772 and maximum Absolute Percentage Error (APE) is 31.14988 The mean APE (MAPE) is 15.64559 530 Fig.7 Performance plot Fig 9.Actual and Estimated Price for 15th April 2010 Fig 10 Actual and Estimated Price for 16th April 2010 Fig 11 Actual and Estimated Price for 17th April 2010 Fig 12 Actual and Estimated Price for 18th April 2010 531 Comparison between actual and estimated price values for 17th April 2010 shown in figure 11 It is observed that minimum percentage error (APE) is 0.421196 and maximum APE is 36.98921 The mean APE (MAPE) is 14.351833728 Comparison between actual and estimated price values for 18th April 2010 shown in figure 12 It is observed that minimum percentage error (APE) is 3.256485 and maximum APE is 33.360 33.89013 The mean APE (MAPE) is 17.6309 V CONCLUSION AND FUTURE SCOPE The conclusion of the proposed system is based on the results obtained from the proposed model The experimental results show the reasonably good forecast results These results are taken when there is no much fluctuation between each hour and days This work is an attempt to the study and analyses the market prices in day ahead market with reference to Indian electricity market The data is available on market clearing prices (MCP) Indian Energy Exchange (IEX) and Power Exchange India Limited (PXIL) The Artificial Neural network (ANN) is developed using last two years data to predict hourly market price Results obtained from neural network model are satisfactory The tool used for developing this proposed work is ANN in Matlab software It is used for training the proposed ANN model The performance of the forecasting model can be improved by considering the various parameters affecting the price volatility and also by using more historical price data which will indicate the behavior of price volatility in more detail This work will be further improved to increase the efficiency in forecasting the electricity price in day ahead market using the support vector machine tool in Matlab [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] ACKNOWLEDGMENT We thank the Department of Electrical Engineering, G.H Raisoni Institute of Engineering and Technology, Savitribai Phule Pune University, Pune, Maharashtra, India for permitting us to use the different computational facilities for this research and development work [17] [18] [19] REFERENCES [1] [2] [3] [4] [5] “An ARIMA Approach to Forecasting Electricity Price with Accuracy Improvement by Predicted Errors”, Ming Zhou, Student Member, IEEE, Zheng Yan, Yixin Ni, Senior Member, IEEE and Gengyin Li, Member, IEEE “Several-Hours-Ahead Electricity Price and Load Forecasting Using Neural Networks”, ParasMandal, TomonobuSenjyu, Member, IEEE, Katsumi Uezato, and Toshihisa Funabashi, Senior Member, IEEE “Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach” M Zhou, Z Yan, Y.X Ni, G Li and Y Nie “Electricity Price Forecasting based on GARCH Model in Deregulated Market”, ZHENG Hua, XIE Li, ZHANG Li-zi “Day-Ahead Electricity Price Forecasting in a Grid Environment”, Guang Li, Student Member, IEEE, Chen-Ching Liu, Fellow, IEEE, [20] [21] [22] Chris Mattson, and Jacques Lawarrée, IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 22, NO 1, FEBRUARY 2007 Jun Hua Zhao, Zhao Yang Dong, Member IEEE, Xue Li, Member IEEE and Kit Po Wong, Fellow IEEE, “A General Method for Electricity Market Price Spike Analysis” 2005 IEEE Wei Sun, Jian-Chang Lu, Ming Meng, Department of Economics & Management, North China Electric Power University, Baoding, Hebei 071003, China E-MAIL: sunweichina@sohu.com, “Application Of Time Series Based SVM Model On Next-Day Electricity Price Forecasting Under Deregulated Power Market”, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006 S Fan, C Mao and L Chen “Next-Day Electricity-Price Forecasting Using A Hybrid Network” IET Gener Transm Distrib., Vol 1, No 1, January 2007 Jun Hua Zhao, Student Member IEEE, Zhao Yang Dong, Senior Member, IEEE, Xue Li, Member, IEEE, and Kit Po Wong, Fellow, IEEE “A Framework for Electricity Price Spike Analysis With Advanced Data Mining Methods” IEEE Transactions On Power Systems, Vol 22, No 1, February 2007 J Bastin, J Zhu, V Banunarayanan, R Mukerji" Forecasting energy prices in a competitive market", IEEE Computer Applications in Power, July 1999 B R Szkuta, L A Sanabria and T S Dillon, "Electricity Price ShortTerm Forecasting using Artificial Neural Networks", IEEE Transactions on Power System, Vol 14, no 3, pp 851-857, 1999 Wei Sun, Jie Zhang School of Business and Administration, North China Electric Power University, Baoding, 071003, P.R China abcjie1984@163.com, 2008 International Conference on Internet Computing in Science and Engineering Yi Wang, School of Business Management, North China Electric Power University, Beijing, 102206, China, Songqing Yu2, Shandong Linqing Power Supply Company, Linqing, 252600, China, “Price Forecasting by ICA-SVM in the Competitive Electricity Market” 2008 IEEE Jianhua Zhang Jian Han Rui Wang GuolianHou, “Day-ahead Electricity Price Forecasting Based on Rolling Time Series and Least SquareSupport Vector Machine Model”, IEEE 2011 Modelling Power Plants Emission Constraints, WSEAS Transactions on Systems, Vol.3, Issue 7, 2004, pp 2512-2516 V Mendes, J.C Quadrado, J Catalão, S Mariano and L Ferreira, Environmental Concerns on Fossil Fuel Plants Emission for Power Engineering Education, WSEAS Transactions on Advances in Engineering Educations, Vol.1, Issue 1, 2004, pp 1-5 F.J Nogales, J Contreras, A.J Conejo and R Espínola, Forecasting Next-Day Electricity Prices by Time Series Models, IEEE Transactions on Power Systems, Vol.17, No.2, 2002, pp 342-348 J Contreras, R Espínola, F.J Nogales and A.J Conejo, ARIMA Models to Predict Next-Day Electricity Prices, IEEE Transactions on Power Systems, Vol.18, No.3, 2003, pp 1014-1020 O.B Fosso, A Gjelsvik, A Haugstad, B Mo and I Wangensteen, Generation Scheduling in a Deregulated System, IEEE Transactions on Power Systems, Vol.14, No.1, 1999, pp 75-81 A.J Wang and B Ramsay, A Neural Network Based Estimator for Electricity Spot-Pricing with Particular Reference to Weekend and Public Holidays, Neurocomputing, Vol.23, Issue 1, 1998, pp 47-57 B.R Szkuta, L.A Sanabria and T.S Dillon, Electricity Price Short-Term Forecasting Using Artificial Neural Networks, IEEE Transactions on Power Systems, Vol.14, No.3, 1999, pp 851-857 F.Gao, X Guan, X.-R Cao and A Papalexopoulos, Forecasting Power Market Clearing Price and Quantity Using a Neural Network Method, in Proc IEEE Power Engineering Society Summer Meeting, Seattle, WA, 2000, pp 2183-2188 532 ... Forecasting in Day Ahead Market by using Artificial Neural Network (ANN) The training data is to be taken firstly The training is started with one hidden node and it is increased one by one increasing... Business Management, North China Electric Power University, Beijing, 102206, China, Songqing Yu2, Shandong Linqing Power Supply Company, Linqing, 252600, China, Price Forecasting by ICA-SVM in. .. neural network during training The training data is further proceeding to the ANN model to estimate the price Figure shows ANN model for trained 529 prices estimation By using this model future prices