Abstract
I. Introduction
II. Deterministic Wind Speed and Power Forecasting Classification
III. Methodology
IV. Conclusion
Authors
Figures
References
Abstract
The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, time-scales, power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.
Introduction
In recent years, wind power is the most competitively priced technology in many markets. According to Global Wind Energy Council (GWEC) Annual Report 2018 [1], the cumulative wind power installed during 2001 to 2018 is 591 GW that is expected to reach 908 GW by the end of 2023 as shown in Fig. 1. Despite providing more than half of renewables growth [2], the intermittent nature of wind raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. The challenges that raised to the power system due to the intermittent nature of wind includes planning and operational difficulties, quality of power, and standard of inter-connections. For example, the system operator needs to allocate additional energy reserves in case any power fluctuation occurs between programmed and actual power produced. This additional reserves would increase the operational costs, which subsequently increases the final energy prices [3]. Albadi and Saadany discussed a detailed review of wind power intermittency impacts on power systems [4]. One solution to overcome this problem is wind energy forecasting. A precise forecast would help to develop appropriate incentives and well-functioning hour-a-head or dayahead electric markets [5].