LONG TERM LOADFORECASTING
FOR SOUTHERN GRID INDIAN POWER
SYSTEM USNIG ANN APPROACH FOR
TRANSMISSION EXPANSION
PLANNING
Mr G SRINIVASULU
Associate Professor, Department of EEE, Narayana Engineering College,
Nellore–AP- 524004. gs_meghana@rediffmail.com,
Phone Number: 9490166188
Dr B SUBRAMANYAM
Professor, Department of EEE, PBRVITS, Kavali – AP-524201. bsubramanyamb@gmail.com
Dr M Surya Kalavathi
Professor, Department of EEE, JNTUH, Hyderabad – TS – 500085.
munagala12@gmail.com
1. Abstract
Day to day operation and planning actions of power systems necessitates the prediction of the future electrical demand of its customers and prediction of load demand is called as Load Forecasting. Usually Load Forecasting is divided into short term, medium term, and long term forecasting. The short-term load forecasting refers to hourly prediction of the load for a time ranging from one hour to several days. The medium term load forecasting forecasts for a forecast horizon of one to several months ahead. The long term forecasting refers to forecasts prepared for one to several years in the future. Load Forecasting is more useful in planning of power systems i.e. Generation Expansion Planning, Transmission Expansion Planning, and Load Scheduling etc. In this paper, Long Term Load Forecasting is done for Southern Grid Indian Power System using Artificial Neural Networks (ANN). Load Forecasting is prepared for the proposed system for 11 years i.e. from year 2015 to year 2025.
Keywords: Load Forecasting, ANN Approach, Southern Grid Indian Power Systems, Load Demand, Peak Load.
2. Introduction
The importance of accurate load forecasts will increase in the future because of the dramatic changes occurring in the structure of the utility industry due to deregulation and competition. This environment compels the utilities to operate at the highest possible efficiency, which, as indicated above, requires accurate load forecasts. Moreover, the advent of open access to transmission and distribution systems calls for new actions such as posting the available transmission capacity (ATC), which will depend on the load forecasts. In the deregulated environment, utilities are not the only entities that need load forecasts. Power marketers, load aggregators, and independent system operators (ISO) will all need to generate load forecasts as an integral part of their operation.
Accordingly, different methods of load forecast have to be applied, depending on the planning horizon and thus on the voltage level and/or task of planning. From a number of different load forecasting procedures, five methods are described below.
Load forecast with load increase factors
Load forecast based on economic characteristic data
Load forecast with estimated values
Load forecast based on specific load values and extend of electrification
Load forecast with standardized load curves.
The precise application of the different methods cannot be determined exactly and combinations are quite usual.
In this paper, an Artificial Neural Network based Load Forecasting scheme is developed by collecting the peak load data for a period from 2006 to 2014 as training input.
3. Artificial Neural Network (ANN) based Load Forecasting
Artificial Neural Networks (ANN) are systems calculated based on the motivation obtained from the working of human brain. A typical ANN comprises set of Arithmetic computing units called as nodes or neurons. These nodes are connected to each other in a network of interconnected layers. A classical node of ANN is as shown in the figure (1).
Figure (1): Classical node of ANN
Each and every interconnection has its own weight Wij indicating the potency of interconnection. The input represented by Xi gets multiplied by the weight Wij while arriving at node j through the individual interconnection. All the individual inputs are first added inside the neuron while passing through a non linear single input single output function to generate the output of the neuron. The output is further promulgated to other neurons via consequent interconnections. One of the important assets of ANN is its capability to form difficult and non linear associations between input and output vectors. Learning process is important criteria in Neural Networks for training the Neural Networks study by altering or assuming the weights by comparing output of ANN to the targeted output.
Once an ANN is trained the pulled out information leftover in the resulting connection in a distributed manner. A trained ANN can produce generalized output even though the input is not accurately similar as anyone of those used in the training data. This ability to generalize intends ANN is perfect for forecasting applications where there is accessibility of past data but the forecast providers do not equivalent exactly with those data. This property is utilized in this paper to design a long term load forecasting system for Southern Grid Indian Power System.
4. Results
days in every month for the period 2006 to 2014 are used. All most around 1200 data points are utilized for training and confirmation. The training data points for the month of April during the year 2006 given in Table (1):
Table (1): Training data points for month of April 2006
April 2006
Training Input (peak load in MW) Target output (peak load in MW)
24434 24434 24041 24041 24019 24019 23890 23890 23763 23763 23568 23568 23694 23694 23657 23657 23612 23612 23283 23283
The load for this period is taken from the website SRLDC.IN. Forecast year and the month are taken as inputs to design the system to forecast the load for a particular year. The maximum of forecast obtained for a particular month can be taken as the forecast for that specific year.
The result of the forecast for the years 2015, 2020 and 2025 are given in the table (2) in comparison with base year 2013.
Table (2): Percentage hike of load with respect to base year 2013
Year Forecast Load (MW) % increase in Load (Base year 2013)
2015 40887.5 12.55
2020 50081.3 37.858
2025 59275 63.166
Note: Peak load in 2013 is 39000MW
To observe the accuracy of the forecasted load is compared with the historic data available for the years 2007, 2009 and 2014 as provided in the table (3).
Table (3): Comparison of forecasted load with historic data
Year Forecast Load Actual Load Error (%)
2007 26215 26121 0.359 2009 29937 29856 0.271 2014 38728 38626 0.264
In order to further validation of the system the data points provided from 2006 to 2010 are considered in the training set and load was forecasted for years 2011, 2012, 2013 and 2014. The results are tabulated in the table (4).
Table (4): Comparison of forecasted load with Actual load
Year Forecast Load Actual Load Error (%)
ANN To figure (1) line). Fig results ob which sh Forecasti
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Figure (3): Regression plot for Forecasting of load for year 2010
Figure (4): Training Set for Forecasting of load for year 2010
Figure (6): Regression plot for Forecasting of load for year 2015
Figure (7): Training Set for Forecasting of load for year 2015
Figure (8): Performance plot for Forecasting of load for year 2015
Table (5): Forecasted Load from year 2015 to 2025
Figure (6) shows the Regression plot for Forecasting of load for year 2015 which indicates the accuracy of results drawn from an ANN Tool. Figure (7) indicates the training set plot for Forecasting of load for year 2015 which shows how the training was done to get forecasting of load. Figure (8) gives the performance plot for Forecasting of load for year 2015 which indicates the performance of an ANN Tool.
Table (3) and Table (4) shows how exactly an ANN Tool is predicting load when comparing with historic data and error also very less. So, an ANN Tool for Load forecasting gives better results. Table (5) gives forecasting of load from year 2015 to 2025 monthly wise and it shows predicted peak load in each and every year. By observing Table (5), there is a hike of load 63.166% compared with base year 2013 and average hike per year is 5.742%.
5. CONCLUSION
In this paper, an ANN Tool for Load Forecasting was prepared. Load forecasting for Southern Grid Indian Power System was predicted from year 2015 to 2025 from available historic data from year 2006 to 2014 using ANN Tool. Initially load was forecasted from year 2006 to 2014 to validate the Tool and it was observed that load predicted during this period has less error. Therefore, proposed ANN Toll can be used for Load Forecasting. It can be concluded that, there is a hike of load 63.166% compared with base year 2013 and average hike per year is 5.742%.
6. REFERENCES
[1] A Text Book by Hosseein Siefi and Mohammad Sadegh Sepasian, “Electric Power System Planning”, Springer-Verlag Berlin Heidelberg 2011.
[2] A Text Book by H.Lee Willis and Muhammad H.Rashid, “Understanding Electric Utilities and Deregulation” , CRC Press, Taylor & Francis Group, 2006.
[3] A Text Book by Jack Casazza and Frank Delea, “Understanding Electric Power Systems”, John Wiley & Sons, Inc., Hoboken, New Jersey, 2003.
[4] T. De la Torre, J. W. Feltes, T. G. S. Roman, and H. M. Merril, “Deregulation, privatization, and competition: transmission planning under uncertainty,” IEEE Trans. PWRS, Vol. 14, No. 2, pp. 460-465,May 1999.
[5] A Text Book by Juergen Schlabbach and Karl-Heinz Rofalski, “Power System Engineering”, Wiley-Vch, Verlag, GmbH & Co, KGaA, Weinheim, 2008.
[6] A Text Book by Mohamad Hassoun “Fundamentals of Artificial Neural Networks”, MIT Press, 1995.
[7] Park, D.C., El-Sharkawi, M.A., Marks, R.J. and Atlas, L.E, “Electric load forecasting using an artificial neural network”, IEEE Transaction on Power Systems, Vol. 6, Issue 2, PP 442-449, May 1991.
[8] Henrique Steinherz Hippert, Carlos Eduardo Pedreira, and Reinaldo Castro Souza, “Neural Networks for Short-Term Load Forecasting: A Review and Evaluation”, IEEE Transaction on Power Systems, Vol. 16, Issue 1, PP 44-55, February 2001.
[9] Hobbs, N.J., Kim, B.H. and Lee, K.Y., “Long-Term Load Forecasting Using System Type Neural Network Architecture”, International Conference on Intelligent Systems Applications to Power Systems, PP 1-7, November 2007.
[10] Daneshi, H., Shahidehpour, and M. ; Choobbari, A.L., “Long-term load forecasting in electricity market”, IEEE International Conference on Electro Information Technology, PP 395-400, May 2008.
Month /
year 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
January 40604 41622 43351 45219 47025 49399 50856 52683.6 54792 56917 57985
February 40805 42444 44170 46103 47839 49716 51387 53035.6 55262 57390 58182
March 40887 42713 44524 46374 48190 50054 51804 53535.2 55533 57436 59112
April 40807 42726 44565 46403 48240 50081 51906 53734.5 55589 57276 59269
May 40667 42580 44563 46380 48242 50077 51920 53758.8 55597 57228 59274
June 40466 42041 44409 46224 48221 50069 51901 53732.4 55597 57347 59275
July 40190 41323 42392 45736 48074 50055 51838 53672.2 55592 57145 59273
August 39851 40928 41366 45165 47511 50033 51759 53623.7 55576 56892 59237
September 39688 40922 42012 44992 46902 50000 51656 53564.3 55521 56379 58857
October 39745 40956 42233 44887 46615 49995 51552 53505.6 55382 56077 57895
November 39466 40901 42079 44779 46301 49961 51435 53467.7 55210 56076 57028
December 39111 40813 41808 44693 45912 49841 51235 53423.6 54925 56076 56564
