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Short Term Load Prediction in Distributed System Using Machine Learning

Authors(3):

V. Kalaiarasi, A. Helana, M. Sakthivel
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Accurate forecasts of electrical substations are mandatory for the efficiency of the Advanced Distribution Automation functions in distribution systems. Artificial Neural Network (ANN) model have been extensively implemented to produce accurate results for short-term load forecasting with time lead ranging from an hour to a week. The proposed system describes the design of a class of machine-learning models, namely neural networks, for the load forecasts of medium-voltage/low-voltage substations. Four weather seasons are defined by the Meteorological Department, India. Each season includes the group of month. Representative months are selected from each season by observing the variation in load behavior patterns. An input vector composed of load and temperature values at previous instants, is employed to train ANN designed for each selected month by using Back-Propagation algorithm with Momentum learning rule. We focus on the methodology of neural network model design in order to obtain a model that has the best achievable predictive ability given the available data. ANN testing is carried out and their performance is evaluated using mean absolute percentage error (MAPE) criterion. Finally, error values are compared for each month and hence the deviation in forecasting ability of ANN is observed for each month and season.

V. Kalaiarasi, A. Helana, M. Sakthivel

Artificial Neural network, Short-term Load Forecasting, Back Propagation Algorithm

  1. Pang Qingle, and Zhang Min, “Very Short-Term Load Forecasting Based on Neural Network and Rough Set,” College of Computer Science Liaocheng University Liaocheng, Shandong, 252059, China
  2. Gautham P. Das, Chandrasekar S., Piyush Chandra Ojha, “Artificial Neural Network Based Short Term Load Forecasting for the Distribution Utilities,” Kalki Communication Technologies Limited., Bangalore,.
  3. P. Zhang is with NARI Accenture Information Technology Center, Beijing 100044, China. X. Y. Wu, X. J. Wang, and S. Bi are with School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China,. “Short-Term Load Forecasting Based on Big Data Technologies,” Manuscript received March 27, 2015; revised June 19, 2015; accepted August 9, 2015. Date of publication September 30, 2015; date of current version August 19, 2015.
  4. Ni Ding, Clémentine Benoit, Guillaume Foggia, Yvon Bésanger, Senior Member, IEEE, and Frédéric Wurtz, “Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems,” Manuscript received February 20, 2014; revised May 21, 2014, August 25, 2014, December 04, 2014; accepted December 27, 2014. Paper no. TPWRS- 00247-2014.
  5. R.Campo and P.Ruiz,”Adaptive weather-sensitive short term load forecast”, IEEE Summer Power Meeting, 1986.
  6. D.M.Falcao and U.H.Bezerra, “Short term forecasting of nodal active and reactive load in electric power systems”,
  7. Proc. 2nd IEE Int. Conf. on Power Systems Monitering and Control ,pp.18-22,1986.
  8. Gross.G, and Galiana,F.D, “Short term load forecasting,” Pacific Gas & Electric Co., san Francisco, CA, US

Publication Details

Published in : Volume 2 | Issue 2 | March-April - 2016
Date of Publication Print ISSN Online ISSN
2016-04-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
403-407 IJSRSET162231   Technoscience Academy

Cite This Article

V. Kalaiarasi, A. Helana, M. Sakthivel, "Short Term Load Prediction in Distributed System Using Machine Learning", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.403-407, March-April-2016.
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