Efficient Prediction of Dynamic Tariff in Smart Grid Using CGP Evolved Artificial Neural Networks

The phenomenal growth of smart grids is resulting in their ever increasing adaptation which has resulted in opening doors to extensive research for applications incorporated within the grid environment. A smart electricity price forecasting mechanism is proposed which when incorporated in the smart grid can be quite beneficial in informing the user of the electricity price during the next hour. Two models have been evolved using the Neuro Evolutionary Cartesian Genetic Programming Evolved Artificial Neural Network(CGPANN) algorithm to estimate the electricity prices for the next hour. Both the models incorporate Feedforward CGPANN algorithm. One of these models takes in as input electricity prices of the previous 12 hours to predict the price value of the next hour, while the other takes in as input the price value of the previous 24 hours to predict the electricity unit cost during the next hour. Comparison of the techniques with previous methods show exceptional strength of prediction. An error as low as 2.82% has clearly established the proposed FCGPANN based forecasting method as an efficient method for futuristic electricity price forecasting. Moreover such prediction can be quite beneficial in demand side management in smart grid environment as informing the user of the rate of electric unit during the next hour may help the user in reducing extra power utilization resulting in a cost effective solution.

[1]  A. Feliachi,et al.  Effects of dynamic pricing on residential electricity bill , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[2]  Hamid Gharavi.  Smart Grid: The Electric Energy System of the Future , 2011 .

[3]  Olivier Grunder,et al.  Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm , 2017 .

[4]  Akbar Maleki,et al.  Electricity price forecasting using neural networks with an improved iterative training algorithm , 2018 .

[5]  W. Marsden.  I and J , 2012 .

[6]  A.J. Conejo,et al.  Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[7]  Risto Miikkulainen,et al.  Efficient evolution of neural network topologies , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Risto Miikkulainen,et al.  Accelerated Neural Evolution through Cooperatively Coevolved Synapses , 2008, J. Mach. Learn. Res..

[9]  Vassilis S. Kodogiannis,et al.  Electricity price forecasting using asymmetric fuzzy neural network systems , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[10]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[11]  Jürgen Schmidhuber,et al.  Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning , 2005, IJCAI.

[12]  Narayanan Kumarappan,et al.  Day-Ahead Deregulated Electricity Market Price Forecasting Using Recurrent Neural Network , 2013, IEEE Systems Journal.

[13]  Gul Muhammad Khan,et al.  Evolution of neural networks using Cartesian Genetic Programming , 2010, IEEE Congress on Evolutionary Computation.

[14]  Risto Miikkulainen,et al.  Real-time neuroevolution in the NERO video game , 2005, IEEE Transactions on Evolutionary Computation.

[15]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[16]  Saifur Rahman,et al.  Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration , 2010, IEEE PES T&D 2010.

[17]  N. Amjady.  Day-ahead price forecasting of electricity markets by a new fuzzy neural network , 2006, IEEE Transactions on Power Systems.

[18]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[19]  Julian Francis Miller,et al.  Cartesian genetic programming , 2009, GECCO.

[20]  Jose Ignacio Moreno,et al.  Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project , 2017 .

[21]  Julian Francis Miller,et al.  Cartesian genetic programming , 2010, GECCO.

[22]  Risto Miikkulainen,et al.  Efficient Reinforcement Learning through Symbiotic Evolution , 1996, Machine Learning.

[23]  Zuyi Li,et al.  Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets , 2004 .

[24]  Chen-Ching Liu,et al.  Day-Ahead Electricity Price Forecasting in a Grid Environment , 2007, IEEE Transactions on Power Systems.

[26]  Bin Hu,et al.  Multigate Communication Network for Smart Grid , 2011, Proceedings of the IEEE.