LOAD FORECASTING MODEL FOR ENERGY MANAGEMENT SYSTEM USING ELMAN NEURAL NETWORK
Electric load forecasting is used for forecasting of future electric loads. Since the economy and reliability of operations of a power system are greatly affected by electric load, cost savings mainly depend on load forecasting accuracy. An accurate system load forecasting which is used to calculate short-term electric load forecasts, is an essential component of any Energy Management System (EMS). This can be improved by making use of Artificial Neural Networks (ANN). Existing Boosted Neural Networks (BooNN) technique helps in reduction of forecasting errors and variation in forecasting accuracy. However it is not flexible to rapid load changes.In the proposed work, Elman Neural Network technique is considered. This technique improves the load forecasting accuracy. The proposed method is implemented in IEEE 14 bus system. Simulation results showed that this method has increased the Voltage profile and also the active power losses have been reduced. Overall power transfer capability has been improved. Also the computational time has been minimized when compared to the existing techniques.
 Wenbo Shi,Na Li,Chi-cheng chu and Rajit Gadh,”Real-Time Energy Management in Microgrids,” IEEE Trans. Smart Grid, vol. 8, no. 1, pp. 228-238, Jan. 2017.
 P. Mandal, T. Senjyu, K. Uezato, T. Funabashi, Forecasting several-hours-ahead electricity demand using neural network, in: IEEE Conference on Power Systems, vol. 2, 2004, April, pp. 515–521.
 K. Methaprayoon, Neural Network-Based Short Term Load Forecasting for Unit Commitment Scheduling (M.S. thesis), The University of Texas at Arlington, 2003.
 S. Pandian, K. Duraiswamy, C. Rajan, N. Kanagaraj, Fuzzy approach for shortterm load forecasting, Electr. Power Syst. Res. 76 (6–7) (2006) 541–548.
 T. Haida, S. Muto, Regression based peak load forecasting using a transformation technique, IEEE Trans. Power Syst. 9 (4) (1994) 1788–1794.
 G. M. Khan, F. Zafari, and S. A. Mahmud, “Very Short Term Load Forecasting Using Cartesian Genetic Programming Evolved Recurrent Neural Networks (CGPRNN),” 2013 12th International Conference on Machine Learning and Applications, pp. 152–155, 2013.
 S. Fan and R. J. Hyndman, “Short-term load forecasting based on a semi-parametric additive model,” IEEE Trans. Power Syst., vol. 27, no. 1, pp.134–141, 2012.
 Xu L, Chen W J. Short-term load forecasting techniques using ANN[C]. IEEE International Conference on Control Applications, 2001: 157-160.
 Zhu J P. The application of neural network in power load forecasting[J]. Science and Technology Information, 2015, 13(23): 32-32.
 Zhou Y, Yin B D, Ren L. The network short-term load forecasting model based on BP neural network research[J]. The Electric Measurement and Instrument, 2011, 48(2): 6871.
 Peng X G, HuS F, Lv D Y. The short-term load forecasting method based on RBF neural network were reviewed[J]. Power System Protection and Control, 2011, 39(17): 144148.
 Sui H H. The Short-term power load forecasting based on BP neural network research[D]. Harbin Institute of Technology, 2015.
 Shi H X, He Y, Dong X H.The power load forecasting model based on Elman neural network research[J]. Industrial Instrumentation and Automation Devices, 2013, (1): 85-87.
 Yu D L, Zhang Z S,Han S X. The research of the demand response Elman-NN short-term load forecasting model[J]. New Technology of Electrical Power, 2017, (4): 59-65.