Load forecasting of G.B. Pant University of Agriculture & Technology, Pantnagar using Artificial Neural Network

Transcription

1 Load forecasting of G.B. Pant University of Agriculture & Technology, Pantnagar using Artificial Neural Network VIKRAM VEER SINGH 1 Electrical Engineering Department College of Technology GBPUAT, Pantnagar, Uttarakhand Village- Jamalpur Kalan, Po-Jwalapur Haridwar, Uttarakhand INDIA vikram.chaudhary14@gmail.com AJAY SRIVASTAVA 2 Head, Electrical Engineering Department College of Technology GBPUAT, Pantnagar, Uttarakhand INDIA drajay16@gmail.com Abstract: - Load forecasting is the technique for prediction of electrical load. In a deregulated market it is important for a generating company to know about the market load demand for generating near to accurate power. The supplying and producing companies draw special attention towards the forecasting systems as they neither want to produce more than what is to be supplied nor they want to produce less so as to avoid non-satisfaction to customers. Its applications also includes load switching, contract evaluation, and infrastructure development. Artificial intelligence neural network technique have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This paper presents the development of an ANN-based Short Term Load Forecasting (STLF) model for G.B. Pant University of Agriculture and Technology, Pantnagar (G.B.P.U.A.T), Uttarakhand, India using MATLAB. The proposed ANN is trained with weatherrelated data, data regarding holiday and historical electric load-related data using the data from August 2008 to August Key-Words: - Load Forecasting, Artificial Neural Network, MAPE. 1 Introduction Electrical load forecasting is one of the various ways which researchers from various fields of knowledge created to improve the agreement between the production and consumption of energy. Load forecasting plays an important role in power system design, planning and development and it is the base of economic studies of energy distribution and power market. The period of load forecasting can be for one year or month (long-term or mediumterm) and for one day or hour (short -term) [1, 2, 3, and 4]. STLF help to estimate load flows and to make decisions that can prevent overloading [5]. Timely implementation can result to more reliability and to reduced occurrences of equipment failures and blackout. Various research activities in ANN have shown that they possess powerful pattern recognition and pattern classification capabilities. The required data was collected from three sources, Pantnagar electric substation near dairy farm provided all the electric load related data, Central Institute of Medical and Aromatic Plants Research Centre (CRC), Pantnagar provided all weather related data and Registrar Office Pantnagar provided the information about holidays. Inputs to the ANN include past loads, temperature, rainfall, sunshine, holiday or working day etc. and output is load for a particular day. The load forecasted by the proposed model is of daily type [6]. 2 Identification of input variables The university campus is located at a distance of ISBN:

2 250 km from Delhi in Udham Singh Nagar district of Uttarakhand. University houses ten colleges, and various primary and secondary level schools and 23 hostels (called bhawans) are also located on the university campus. The University campus at Pantnagar is spread in an area of 10, acre ( km 2 ). finally used which can be seen from figure1. Electric load for that particular day was used as output to the model. 3 The Proposed Approach Fig.1: Input vector configuration There are many factors affecting the load of this area, which make the forecasting unique and challenging. The weather change affect the load demand due to a huge load in the system, day of the week effects the consumption of the load. Another important factor to be considered is whether it is a holiday or a working day, as it is a university campus whenever there is a holiday it effects the load consumption. Holidays are more difficult to forecast than non-holidays because of their relative infrequent and sometimes sudden occurrence. Past loads also effect the forecasting so previous day s load and previous to previous day s load is also considered [7].Selection of proper input variables for the model is one of the most important part of this type of research work [8]. So after proper analysis of the complete data available it was decided which factors to involve as input data. A total of nine input variables were 3.1 Methodology Used The use of ANN has been a widely studied electric load forecasting technique since The outputs of an artificial neural network are some linear or nonlinear mathematical function of its inputs [9]. The inputs may be the outputs of other network elements as well as actual network inputs. In practice network elements are arranged in a relatively small number of connected layers of elements between network inputs and outputs. Feedback paths are sometimes used. As already mentioned the most popular artificial neural network architecture for electric load forecasting is back propagation. Back propagation neural networks use continuously valued functions and supervised learning. That is, under supervised learning, the actual numerical weights assigned to element inputs are determined by matching historical data (such as time and weather) to desired outputs (such as historical electric loads) in a pre-operational training session. Artificial neural networks with unsupervised learning do not require preoperational training. So far, there is no single model or algorithm that is superior for all utilities. The reason is that utility service areas vary in differing mixtures of industrial, commercial, and residential customers. They also vary in geographic, climatologic, economic, and social characteristics. Selecting the most suitable algorithm by a utility can be done by testing the algorithms on real data. In fact, some utility companies use several load forecasting methods in parallel. As far as we know, nothing is known on a prior conditions that could detect which forecasting method is more suitable for a given load area. An important question is to investigate the sensitivity of the load forecasting algorithms and models to the number of customers, characteristics of the area, energy prices, and other factors. ISBN:

3 3.2 Software used MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. It can be used to analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. This software has well defined syntax for different operations which provides user a friendly environment. One of its toolbox is Neural Network Toolbox. It provides functions for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox we can design, train, visualize, and simulate neural networks. We can use Neural Network Toolbox for applications such as pattern recognition, data fitting, clustering, time-series prediction, and dynamic system modelling and control. 3.3 Range of Data The overall data is of daily type taken for a complete range from August 2008 to August Apart from the weather related data, Numerical indexes of [1:12] were given to represent the month, [1:7] to represent the day in which 1 represents Monday and 7 for Sunday and [0:1] to represent holiday or working day. 3.3 Hidden Layer In the proposed ANN model, it is found that for the available data set two hidden layers shows better results for the learning of the network. The number of neurons in the hidden layer has the greatest effect in the network performance. If there are no enough hidden neurons, the network will find difficulty in the learning. On the other hand, the more hidden neurons, the more capability network to over-fit the trained data. In our experiments, varying the number of hidden neurons has led us to the set with the smallest error. 3.4 Training Back-propagation is a systematic method for training multilayer artificial neural network. It is one of the most popular and robust tools in the training of artificial neural networks. It has a mathematical foundation that is strong if not highly practical [10]. Back propagation algorithm is capable of handling very large learning problems which makes it a very popular algorithm. It is a multilayer forward network using extend gradient-descent based delta-learning rule commonly known as back propagation (of errors) rule. G.E Hinton, Rumelhart and R.O. Williams first interlaced back propagation in It has been one of the most studied and used algorithms for neural networks since then. Being a gradient descent method with minimizes that total squared error of the output computed by the network. The combination of weights which minimizes the error function is considered to be a solution of the learning problem. Since this method requires computation of the gradient of the error function at each iteration step, we must guarantee the continuity and differentiability of the error function. The network is trained by supervised learning method. The network is to achieve a balance between the ability to respond and the ability to provide good responses to the input that are similar. Back propagation passes error signals backwards through the network during training to update the weights of the network. Because of this dependence on bidirectional data flow during training, back propagation is not a plausible reproduction of biological learning mechanisms. A differentiable activation function makes the function computed by a neural network differentiable (assuming that the integration function at each node is just the sum of the inputs), since the network itself computes only function compositions. The error function also becomes differentiable. Back propagation networks are necessarily multilayer perceptron (usually with one input, one hidden, and one output layer). In order for the hidden layer to serve any useful function, multilayer networks must have non-linear activation functions for the multiple layers: a multilayer network using only linear activation functions is equivalent to some single layer, linear network. Non-linear activation functions that are commonly used include the logistic function, the softmax function, and the gaussian function. ISBN:

4 4 Work Flow 4.1 Collection of data The data required in this research is mainly of three types, that is why it is been collected from three sources. First is the Pantnagar Electric Substation near dairy farm and second is Central Institute of Medical and Aromatic Plants Research Centre (CRC), Pantnagar and third is Registrar Office Pantnagar. The electric load details were collected from substation from August 2008 to August 2013 on daily basis providing details about maximum electrical load of every day over the mentioned period i.e. a total of 1857 days. All the weather related data i.e. temperature (maximum and minimum), rainfall, sunshine hours of each and every day for the required period was collected from CRC Pantnagar. The data about holiday i.e. whether a particular day is working day or an off day in university campus was collected from Registrar Office. The collected data was then fed into excel files. total data was used for training and then the network was tested for nearly 15% i.e. 300 values. The data was tested on various types of networks depending upon the number of hidden layers and number of neurons in the hidden layer. In this paper two models are compared on the basis of Mean Absolute Percentage Error (MAPE). 4.3 Configure the network After a neural network is created the next step is its configuration. This step consist of examining inputs and target data. The configuration step is normally done automatically, when the training function is called. It can also be done manually by calling the configuration function. 4.4 Initialize the weights and biases The weights are initialized randomly at the configuration step. But these weights are not optimal weights for the solution of the problem. At the training time, the weights are updated after each epoch according to the feed forward back propagation algorithm and Levenberg-Marquardt as a training function. The optimal value of the weights are obtained when the six validations are failed in a row. 4.5 Training the network The training style used in neural network toolbox is batch training. In this type of training the weights and biases are only updated after all the inputs are presented. But in some cases incremental training can also be used in which weights and biases are updated each time an input is presented to the network. As already mentioned, the total available data is being divided into two parts i.e. training and testing. So only the training data i.e number of values of the data were used. Fig.2: Complete Flow chart of the work done 4.2 Create the network The data was divided into two type viz. training and testing. In this 1557, i.e. nearly 85% of the 4.6 Validate the network As the data is fed as input, the input vectors and target vectors will be randomly divided, with 70% used for training, 15% for validation and 15% for testing. So 15% of the data fed is used as a validation of the network. 4.7 Use the network The network which is created finally is used to predict the future values of load. As 300 values ISBN:

5 were already kept to test the network for those values on which the network was not trained on. 5 Configuration 5.1 Model-1 The proposed ANN model is a three layer feedforward ANN, it has nine inputs, first hidden layer has nine neurons, second hidden layer has thirty five neurons and one neuron is present in the output layer. These lines are for training, validation, and testing. Training is stopped by two conditions: Six validation failure or 1000 epoch, whichever happens first. Training of this model is stopped at 22 nd epoch because there are six validation failure from the 16 th to the 22 nd epoch. So the best performance is obtained at 16 th epoch, which is Fig.3: Architecture of Model Model-2 On the other hand the second model is a two layer feed-forward ANN, nine inputs are used, first hidden layer has thirty neurons, and one neuron is present in the output layer. In both of the models tan-sigmoid activation function used in the hidden layer, and linear function in the output layer. Fig.5: Performance plot Error Plot Figure 6 shows a small part of the complete error plot. Number of days is depicted on -axis and load is depicted on -axis and it can be seen clearly that forecasted load (blue coloured part) is following the actual load(red coloured part) very precisely. Fig.4: Architecture of Model-2 6 Results 6.1 Model-1 The performance plot shown in figure provides us the information about the performance of the Model-1 during the training which is Mean Squared Error (MSE) corresponding to the epoch on -axis. This is a dynamic plot because it shows the progressing improvement (reducing MSE) of the model-1 while the training is in progress. In figure we can see that there are three lines. Fig.6: A small part of error plot Mean Absolute Percentage Error (MAPE) ISBN:

6 It usually expresses accuracy as a percentage, and is defined by the formula: N: Total number of observations; F k : Forecasted Load; A k : Actual load When computed over training data MAPE=2.59% and when computed over testing data MAPE=3.09%. 6.2 Model-2 It has the performance of which is slightly lower than the previous model. MAPE for training data=2.91% and that for testing data=3.26%. As it is clearly visible from the results a load forecasting model for G.B. Pant University of Agriculture and Technology has been proposed which has two hidden layers and one output layer, Number of neurons in the first hidden layer being nine and in the second layer being thirty five and one neuron is present in the output layer, Activation functions used are tan-sigmoid in the hidden layer and linear function in the output layer. 7 Conclusion Short Term Load Forecasting of G.B.U.A.T Pantnagar has been carried out successfully and future load is forecasted. MATLAB was used for the prediction of load. The results of the proposed model for one day ahead load forecasting for the university campus shows that it has good performance and reasonable prediction accuracy. Forecasting reliabilities were evaluated by computing the mean absolute percentage error between the actual and predicted values. MAPE of 2.59% was achieved. The results suggest that ANN model with the developed structure can perform prediction with least error and finally it could be an important tool for short term load forecasting. References: [1] Huang, S.J. and K.R. Shih, Short term load forecasting via ARMA model identification including non - Gaussian process consideration, IEEE Trans. Power Syst., 2003, 18: [2] Kandil Nahi, Rene Wamkeue, Maarouf saad and Semaan Georges, An efficient approach for short term load forecasting using artificial neural networks, Int. J. Electric Power Energy syst., 2006, 28: [3] Mandal Paras, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa Funabashi, A neural network based several hours ahead electric load forecasting using similar days approach, Int. J. Elect. Power Energy Syst., 2006, 28: [4] Topalli Ayca Kumluca, Ismet Erkmen and Ihsan Topalli, Intelligent short term load forecasting in Turkey, Int. J. Electric. Power Energy Syst., 2006, 28: [5] Gross, G., F. D. Galiana, Short-term load forecasting, Proceedings of the IEEE, 1987, Vol. 75, No. 12, pp [6] K.B. Sahay, Day ahead hourly load and price forecast in ISO New England market using ANN, Annual IEEE India Conference, INDICON, [7] Hippert, H.S., Pedreira, C.E. andsouza, R.C., Neural networks for short-term load forecasting: A review and evaluation, IEEE Trans. Power Syst., 2001, 16(1): [8] Alsayegh, O.A., Short-term load forecasting using seasonal artificial neural networks, International Journal of Power and Energy Syst., 2003, 23(3): [9] K.Y. Lee, Short Term Load Forecasting using an artificial neural network, IEEE Trans. On Power Systems, 1992, Vol. 7, No. 1, pp [10] Mohan B. Tasre, Hourly load forecasting using artificial neural network for a small area, IEEE ICAESM, 2012, pp ISBN: