Electricity Short term Load Forecasting using Elman Recurrent Neural Network

Siddarameshwara N , Lecturer, Dept of E&E, BVBCET, Hubli, India, sidda_hn@bvb.edu

Anup Yelamali . Student, Dept of E&E, BVBCET, Hubli, India,anupyelamali_4@rediffmail.com Kshitiz Byahatti, Student, Dept of E&E, BVBCET, Hubli, India ,kshitij_byahatti@yahoo.com

Abstract- The proposed work aimed to forecasting the load by using Artificial Neural Networks (ANN). Short term load forecasting plays an important role for the planning, economic and reliable operation of power systems. Therefore, many statistical methods have been conventionally used for such forecasting, but it has been difficult to construct a proper functional model. This difficulty can be reduced by using artificial neural networks. A neural network is a machine that is designed to model the way in which the human brain performs a particular task[ ]. The main aim of the proposed work is to design a neural network model called Elman recurrent network by using MATLAB software to simulate the load forecasting. The work also includes comparing the results obtained by a weather sensitive model and a non weather sensitive model.

I. INTRODUCTION

With the skyrocketing growth of power system networks and the increase in their complexity, many factors have become influential in electric power generation, demand or load management. Load forecasting in one of the critical factors for economic operation of power systems. Forecasting of future loads is also important for network planning, infrastructure development and so on [1]. Power system load forecasting can be classified in three categories, namely short-term, medium term and long term forecasting[4]. Short-term load forecasting covers hourly to weekly forecasts. These forecasts are often needed for day by day economic operations of power generation plants. The proposed paper deals with short term load forecasting. The work was carried out in 4 phases: Phase I- Approximation tool, Phase II – Data Collection, Poor or Inadequate Data Treatment, Pre/Post Data Processing, Phase III – Computing and Training, Phase IV - Validation and Results.

II. POWER SYSTEM LOAD FORECASTING

There are various factors that affect the short term load forecasting. These include endogenous and exogenous variables. Endogenous variables include the load at the particular hour and its previous hours whereas exogenous variable include the parameters like temperature, wind speed and humidity. Other than these several other factors affect the STLF. These include Consumer category, Day of the week, Seasonal effects, Holidays etc.

III. ARTIFICIAL NEURAL NETWORKS (ANNS)

In this work the designed model is Elman recurrent network. The organization of a recurrent network is comparable to a human brain network. Unlike multi-layer perceptron, recurrent structure introduces cycles or loops and backward links in the network [6]. Feedback networks are exceptionally dominant and can get extremely convoluted. The behavior of these types on networks is known to be changing continuously until they reach an equilibrium point. This implies the state of the network remains at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures (fig.1) are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organizations [6].

Fig 1. A typical topology of recurrent networks.

IV. DATA COLLECTION AND PRE PROCESSING

Data collection is a significant function of any type of research study. Inaccurate or insufficient data can impact the results of a study and ultimately lead to invalid or skewed results. In this work the actual load data was taken from KPTCL (Karnataka Power Transmission Cooperation Ltd.) for the area called Anand nagar, Hubli. The area is mainly residential and is provided with an 110kv supply. Weather conditions influence the load greatly. Without replicating, the weather data used in this work were provided by the University of Agricultural Sciences, Dharwad. Thus the correlation between weather related conditions and the consumption is conclusively essential, particularly in regions where significant changes in weather conditions are experienced.

2010 International Conference on Advances in Recent Technologies in Communication and Computing

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DOI 10.1109/ARTCom.2010.44

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A. Correlation analysis

The correlation analysis between climatic and load data was also performed to measure the statistical association between variables. Table 1 summarizes the closeness of linear relations defined by correlation coefficients. Mathematical formulation can be defined as follow: suppose, x denotes the consumption and y represents a weather variable, then the population correlation between two variables x and y. ρ(x, y) = Co variance(x, y) / {variance(x) * variance( y)}1/2 Where ρ is called the product moment correction coefficient or simply the correlation coefficient[ ].

Table 1 : Correlation analysis between the load and exogenous

variables.

The sign (+ or -) indicates the direction of the relationship. For this particular analysis, all weather variables excluding humidity indicate inverse relationships. This implies that if one variable increases the other variable decreases [3]. B. Data Storage

Data storage can be implemented in many ways. The methods used to implement the data storage are external methods like database management system (DBMS) or Microsoft excel and conventional method. In this work data collected was stored in Microsoft Excel sheets. The excel sheets were then imported into matlab using the command “xlsread”. C. Data Pre-processing

The excel sheet stores raw or pre-processed weather and load data. However, the data should be normalized prior to presenting them to a model for training or any forecasting attempt. Data scaling is essential due to the fact that neural networks are often vulnerable to raw data, it’s extremely important that data are scaled (typically values between 0 and 1, or -1 and 1) to avoid convergence problems. Data scaling

The scaling of the data was done in order to improve interpretability of network weights and the uniform scaling equalizes initially the importance of variables. The equation given below has certainly been adopted and implemented to normalize both the historical load data and weather data.

Bad data detection and replacement

With no exception, some bad data were detected in the total load data. The bad data replacement was done by taking the average of the previous hour and the next hour. D. Composition of the Input Vector (IV) of the prediction models

There were two input vector composition. One for the non weather sensitive model (fig 2.a) without any details for the exogenous factors and the other for weather sensitive model (fig 2.b) which includes the exogenous factors.

Fig 2.a Fig 2.b

V. APPLICATION OF ANN IN LOAD FORECASTING

In this work, ANN-based models had been developed and presented with actual load data to forecast the load a week in advance. The Elman Recurrent (ER) Neural Network model was developed and then applied to the total load of Anandnagar. The evolved model is either non-weather sensitive or weather sensitive. The network structure for the non-weather sensitive model was defined as follows: (4-10-1) i.e. 4 inputs for past contiguous consumption instances, 10 hidden layer neurons, 1 output neuron for hour by hour forecast. Similarly, the latter network structure was slightly modified to include the effects of the weather related variables. Thus the network structure was then changed to (8-10-1) .

VI. RESULTS AND DISCUSSIONS.

Comparisons between the developed weather sensitive and non-weather sensitive models were made. In addition, the following performance measure functions were employed: mean squared error (MSE) and mean percentage error (MPE) to evaluate the performance of the models. The actual error in kW was also compared.

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A. Network architectural design for the ER model

The network comprises 10 hidden layer neurons and 1 output neuron. For activations, a tan sigmoid transfer function was used for the hidden layer and pure linear one in the output layer. Both momentum factor and learning rate were held constant throughout the training at 0.75 and 0.35 respectively. The training parameter traingdm was used.

B. Elman recurrent non weather sensitive model The model created doesn’t use the weather data i.e. the weather data is excluded since it is a non weather sensitive. The subsequent simulation results, corresponding error, and network performance for this model are shown in Figure 3 (a), (b), and (c) in the sequence order.

Fig.3 (a) Simulated results

Fig.3 (b) The associated actual error (kW)

Here, we forecasted a week ahead load curve. In the above results, we found that there was a close match between the actual load curve and forecasted load curve. The mean square error was found to be 0.0111 and the mean percentage error was found to be 0.0011 for the non weather sensitive model. The performance goal was met at a very early stage of 23 epochs.

Fig 3(c) The performance goal met

B. Elman recurrent weather sensitive model The following weather variables were used: daily average wind speed, humidity, and maximum and minimum temperatures. Some obtained results and actual error in kW are shown in Figure 4 (a) and (b) respectively. The performance of this model is shown in Figure 4 (c). In Figure 4 (b), one observes that the weather sensitive forecasting model performs slightly better than the first one.

4 (a) Simulated results

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4 (b): The associated actual error (kW)

In the above results, we found that there was a close match between the actual load curve and forecasted load curve. The mean square error was found to be 0.0046 and the mean percentage error was found to be 0.000046 for the weather sensitive model. The performance goal was met at a very early stage of 18 epochs.

4 (c) The performance goal met

VI. CONCLUSIONS The main objective of this research project was to provide power system planners with an accurate and reliable short-term load forecasting (STLF) system which may assist to economically optimize power system operations. This work was sequentially arranged, commencing with the general introduction of STLF, some basic requirements of a convenient STLF system, a detailed introduction of the selected technique (ANNs), data collection, and processing, development of models, and finally the obtained application results for Anand nagar.

Future scope The scope of work of this project was specifically limited to off-line training. But for real-time applications, some matters still need to be carefully addressed during model development process. To start with, the existence of bad data (outliers) in the historical load curve as well as in the weather data can affect the accuracy of the forecast negatively. In this work, a manually based strategy for detecting and replacing bad data was employed. However, this approach is inappropriate for real-time implementation, thus a technique aimed at identifying and replacing abnormal data in the input variable curves needs to be automated.

REFERENCES

[1]P.K.Dash, H.P.Satpathy, A.C.Liew, S.Rahman, “A real-time short-term load forecasting system using functional link network”, IEEE Transactions on Power Systems, Vol. 12, No. 2, May 1997. [2]Toshihiro Matsumoto, Sakio Kitamura, Yoshiteru Ueki, Tetsuro Matsui, “Short-term Load Forecasting by Artificial Neural Networks using Individual and Collective Data of Preceding Years” [3]Alex. D. Papalexopoulos Shangyou Hao, Tie-Mao Peng, “short-term system load forecasting using an artificial neural network” [4]K. Y. Lee, Senior Member, Y. T. Cha, J. H. Park, “Short-term load forecasting using an artificial neural network”, IEEE Transactions on Power Systems, Vol. 7. No. 1, February 1992. [5]T. Senjyu, P. Mandal, K. Uezato and T. Funabashi, “Next day load curve forecasting using recurrent neural network structure”, IEEE Proc.-Gener. Transm. Distrib., Vol. 151, No. 3, May 2004 [6]Simaneka Amakali, “Development of models for short-term load forecasting using artificial neural networks”, Cape Peninsula University of Technology Year, 2008 [7]Moghram I, Rahman S (1989) “Analysis and evaluation of five short-term load forecasting techniques”. IEEE Trans Power Syst, 4:1484–1491

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