Load Forecasting Using Neural Network Integrated with Economic Dispatch Problem
High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2363232Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 816
 D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to time series analysis and forecasting. John Wiley & Sons, 2015.
 C. Barbulescu, S. Kilyeni, A. Deacu, G. M. Turi, and M. Moga, "Artificial neural network based monthly load curves forecasting," in Applied Computational Intelligence and Informatics (SACI), 2016 IEEE 11th International Symposium on, 2016, pp. 237-242: IEEE.
 A. Baliyan, K. Gaurav, and S. K. Mishra, "A review of short term load forecasting using artificial neural network models," Procedia Computer Science, vol. 48, pp. 121-125, 2015.
 A. Khwaja, M. Naeem, A. Anpalagan, A. Venetsanopoulos, and B. Venkatesh, "Improved short-term load forecasting using bagged neural networks," Electric Power Systems Research, vol. 125, pp. 109-115, 2015.
 J. S. Al-Sumait and J. K. Sykulski, "Solving economic dispatch problem using hybrid GA-PS-SQP method," 2009.
 C.-T. Su and C.-T. Lin, "New approach with a Hopfield modeling framework to economic dispatch," IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 541-545, 2000.
 R. A. Jabr, A. H. Coonick, and B. J. Cory, "A homogeneous linear programming algorithm for the security constrained economic dispatch problem," IEEE Transactions on power systems, vol. 15, no. 3, pp. 930-936, 2000.
 A. Farag, S. Al-Baiyat, and T. Cheng, "Economic load dispatch multiobjective optimization procedures using linear programming techniques," IEEE Transactions on Power systems, vol. 10, no. 2, pp. 731-738, 1995.
 H. Demuth and M. Beale, "Neural network toolbox, user’s guide version 4, The Mathworks Inc," ed, 2014.
 A. Ashfaq, S. Yingyun, and A. Z. Khan, "Optimization of economic dispatch problem integrated with stochastic demand side response," in Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on, 2014, pp. 116-121: IEEE.