Rahul Sharma Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 8, Issue 7 (Part -III) July 2018, pp 01-09 www.ijera.com DOI: 10.9790/9622-0807030109 1 | Page A Review of Wind Power and Wind Speed Forecasting Rahul Sharma, Diksha Singh Department of Electrical Engineering Madan Mohan Malaviya University of Technology, Gorakhpur, India Department of Electrical Engineering Madan Mohan Malaviya University of Technology, Gorakhpur, India Corresponding Author: Rahul Sharma ABSTRACT:In today‟s world, withthe growing wind powerpenetration in the emerging power system, accurate wind speed forecasting becomes essential. The paper presents time scale classification for wind speed and forecasting of generated wind power and reviews the different techniques involved in wind speed and wind power forecasting, such as artificial neural networks (ANNs), hybrid techniques, etc. It shows trends of temperature, pressure, wind speed, and its direction of different sites around the world and various locations in India for wind power generation. Non-linear relationship between wind speed and wind and the various problems that occur during the wind power and wind speed forecasting are discussed as well. Index Terms:Artificial neural network, wind speedforecasting, wind power forecasting, hybrid techniques. --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 10-07-2018 Date of acceptance: 24-07-2018 --------------------------------------------------------------------------------------------------------------------------------------- I. INTRODUCTION Wind-energy has the potential of a reliable autonomous source of electric power, but due to the intermittency of wind its large scale integration is very challenging. Wind power generation has the advantage of zero-carbon emission, due to which it has been prevailingly implemented around the world. Till now, several countries have initiated wind power projects covering onshore and offshore wind farms as well as distributed wind power integrations but due to the erratic nature of the atmosphere of the earth, there is a great randomness in wind power generated, which acts as a limiting factor for this source of energy. The randomness of wind speed, adds up to the operating costs for the electricity system. It is known that the relation of wind power with wind speed is cubic in nature, which means that any error in wind speed forecast will give a large (cubic) error in wind power [1]-[5]. This paper provides a detailed review on wind speed forecasting based on recent published papers. The contribution of this paper is the classification of wind speed forecasting, trends of different parameters used in wind speed forecasting, an overview of different problems related to wind power and the results of some new and highly efficient models. The paper has been divided in following eight sections. Section II - problems related to wind power. Section III – time scale classification. Section IV - different wind power forecasting methods. Section V - the non-linear relationship of wind power and wind speed. The trends of different parameters related to wind power generation are depicted in Section VI. Section VII presents the results of simulations and Section VIII discusses the conclusion and future work. II. PROBLEMS RELATED TO WIND POWER Wind power is intermittent and is sometimes non-dispatchable whereas its counterpart fossil-based power is completely controllable because the generation of wind power depends on atmospheric conditions and landscape and thus is variable. Wind energy which gets converted into electric power should be consumed immediately as a result the economic value of wind power generation depends upon synchronized timing of load and wind patterns. Wind energy based generators cannot be scheduled to meet variable load [6]-[7]. III. TIME SCALE CLASSIFICATION Time-scale classification of wind forecasting methods is not expressed clearly in the literature [8]-[12]. However, as shown in table 1, wind forecasting time horizon can be divided into four categories: ฀ Very short-term forecasting: it is also known as turbulence time scale. In this horizon, the prediction time period is from a few seconds to 30 minutes ahead. ฀ Short-term forecasting: it is also known as synoptic scale in the spectral gap. In this horizon, the prediction time-period is from 30 minutes to 6 hours ahead. ฀ Medium-term forecasting: it is also known as synoptic scale. In this horizon, the prediction time period is from 6 hours to 1 day ahead. RESEARCH ARTICLE OPEN ACCESS