Weather Sensitive Medium Term Load Forecasting using Artificial Neural Network

Anticipation of future load patterns is very significant for optimal decision making in power system operation and planning. The medium term load forecasting (MTLF) is used for the annual maintenance scheduling, fuel supplies scheduling, load dispatch, planning of generation shifting, planning and expansion of transmission and distribution system etc. In this study, artificial neural network (ANN) approach has been used for medium term load forecasting, in which both structure learning and parameter learning procedures are implemented. The input data is comprised of historical weather sensitive data i.e. temperature, humidity, wind speed, hour of the day, type of the day (weekday, weekend, holiday), month of the year and hourly load data. For structure learning, a comparative study on the multilayer feed forward networks and recurrent networks has been performed. The performance of the network architectures is estimated on the basis of mean square error and training time. For the optimally selected network, parameter learning is performed using supervised learning and the results obtained are reported. Keywords— ANN; forecasting; load; mean square error (MSE); network architecture; weather parameters.

[1]  Yun Lu.  Short-term load forecasting method based on structural neural network , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[2]  Maarouf Saad,et al.  An efficient approach for short term load forecasting using artificial neural networks , 2006 .

[3]  Zainab H. Osman,et al.  Neural network based approach for short-term load forecasting , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[4]  Vipul K. Dabhi,et al.  Rainfall forecasting using neural network: A survey , 2015, 2015 International Conference on Advances in Computer Engineering and Applications.

[5]  James H. Taylor,et al.  Real-time load forecasting by artificial neural networks , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[6]  Afrah Nazir.  A Comparative Study of Different Artificial Neural Networks Based Intrusion Detection Systems , 2013 .

[7]  B. Tech,et al.  Application of Neural Network to Load Forecasting in Nigerian Electrical Power System , 2007 .

[8]  S.F. Ghaderi,et al.  Electricity price forecasting in Iranian electricity market applying Artificial Neural Networks , 2008, 2008 IEEE Canada Electric Power Conference.

[9]  Robert Fullér,et al.  Neural Fuzzy Systems , 1995 .

[10]  Gaurang Panchal,et al.  Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers , 2011 .

[11]  T. Funabashi,et al.  One-Hour-Ahead Load Forecasting Using Neural Networks , 2002 .

[12]  Shangyou Hao,et al.  An implementation of a neural network based load forecasting model for the EMS , 1994 .

[13]  T. Yalcinoz,et al.  Short term and medium term power distribution load forecasting by neural networks , 2005 .