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Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning, energy system balancing, wind farm operation and control, power system planning, scheduling, storage capacity optimization, and enhancing system reliability. This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction, temperature and wind speed to improve modern power system protection, control and management. Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.

Wind speed forecasting is most needed due to its essentiality in wind farm and power system control and planning operation. Due to the increase of energy demands in order to meet the energy requirement wind energy receive a center of attraction because of its huge amount of availability and ecofriendly characteristics. Though numerous researches implemented different wind speed forecasting models, exact forecasting with the greatest accuracy is still thrusting topic in research. This article proposes two fold novel techniques for long-term wind speed forecasting namely improved spike prop algorithm incorporate spiking neural network (SNN) and improved modified grey wolf optimization algorithm (IMGWOA) based hybrid technique (SNN-IMGWOA). Proposed long-term forecasting technique using spiking neural network optimized through improved modified grew wolf optimization algorithm suitability and performance evaluation analyzed and compared with various earlier optimization algorithm namely GA, ES, PSO, ABC, GSA, CS, CSS and GWO and improved spike prop algorithm associated spiking neural network superiority confirmed with comparison between various traditional techniques such as Persistence, ARIMA, BPN, MLPN, RBFN, ELMAN Network and SVM. Simulations performed based on the observed real-time wind data's; numerical results and analyzes prove the virtue of proposed techniques.

The artificial neural network reduces humanity and society’s burden to solve complex problems highly efficiently. Artificial neural networks resemble brain activities based on the acquired training samples used for various applications such as classification, regression, prediction, smart grid, natural language processing, image processing, medical diagnosis, and so on. This paper illustrates the different artificial neural network architectures, types, merits, demerits, and applications. Therefore, this paper provides valuable information to students and researchers to enrich their knowledge about an artificial neural network and research it. This paper also proposed a multilayer-perceptron-neural-network-based solar irradiance forecasting model, an improved backpropagation neural network-based rainfall forecasting model, and an Elman neural network-based temperature forecasting model. The performances of the proposed neural network-based forecasting models are analyzed with various hidden neurons and validated using the acquired real-time meteorological data. The proposed neural network forecasting models achieve rigorous results with reduced errors for the considered applications and aid sustainability.