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ELm.N Neural Network with m.dified Grey Wolf Optim.zer for Enhanced Wind Speed Forecasting

Circuits and System. 2016年第10期7卷 2975-2995页

作者： m. madhiarasan S. N. Deepa Departm.nt of Electrical and Electronics Engineering Anna University Regional Campus Coimbatore India

The scope of this paper is to forecast wind speed. Wind speed, tem.erature, wind direction, relative hum.dity, precipitation of water content and air pressure are the m.in factors m.ke the wind speed forecasting as a com.lex problem.and neural network perform.nce is m.inly influenced by proper hidden layer neuron units. This paper proposes new criteria for appropriate hidden layer neuron unit’s determ.nation and attem.ts a novel hybrid m.thod in order to achieve enhanced wind speed forecasting. This paper proposes the following two m.in innovative contributions 1) both either over fitting or under fitting issues are avoided by m.ans of the proposed new criteria based hidden layer neuron unit’s estim.tion. 2) ELm.N neural network is optim.zed through m.dified Grey Wolf Optim.zer (m.WO). The proposed hybrid m.thod (ELm.N-m.WO) perform.nce, effectiveness is confirm.d by m.ans of the com.arison between Grey Wolf Optim.zer (GWO), Adaptive Gbest-guided Gravitational Search Algorithm.(GGSA), Artificial Bee Colony (ABC), Ant Colony Optim.zation (ACO), Cuckoo Search (CS), Particle Swarm.Optim.zation (PSO), Evolution Strategy (ES), Genetic Algorithm.(GA) algorithm., m.anwhile proposed new criteria effectiveness and precise are verified com.arison with other existing selection criteria. Three real-tim. wind data sets are utilized in order to analysis the perform.nce of the proposed approach. Sim.lation results dem.nstrate that the proposed hybrid m.thod (ELm.N-m.WO) achieve the m.an square error AVG ± STD of 4.1379e-11 ± 1.0567e-15, 6.3073e-11 ± 3.5708e-15 and 7.5840e-11 ± 1.1613e-14 respectively for evaluation on three real-tim. data sets. Hence, the proposed hybrid m.thod is superior, precise, enhance wind speed forecasting than that of other existing m.thods and robust.

关键词： ELm.N Neural Network m.dified Grey Wolf Optim.zer Hidden Layer Neuron Units Forecasting Wind Speed

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Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network

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Protection and Control of m.dern Power System. 2020年第1期5卷 250-258页

作者： m.madhiarasan Independent Researcher No:14Uzhaippaali StreetAnnai Anjugam NagarAyapakkamChennaiTamil Nadu Pin Code-600077India

Environm.ntal considerations have prom.ted the use of renewable energy resources worldwide for reduction of greenhouse gas *** accurate prediction of wind speed plays a m.jor role in environm.ntal planning,energy system.balancing,wind farm.operation and control,power system.planning,scheduling,storage capacity optim.zation,and enhancing system.*** paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network(RRBFNN)possessing the three inputs of wind direction,tem.erature and wind speed to im.rove m.dern power system.protection,control and *** results confirm.that the proposed m.del im.roves the wind speed prediction accuracy with least error when com.ared with other existing prediction m.dels.

关键词： Recursive radial basis function neural network Prediction Horizons Generic Wind speed

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