Improved Prediction of Wind Speed Using Machine Learning

Abstract

In wind energy systems, wind speed estimation plays an important role. For wind energy systems, accurate forecasting of wind direction is essential, but it is challenging because of its variability. In this paper, wind speed prediction is accomplished using a machine learning-based random forest (RF) method. For the production of wind energy, short-term wind speed prediction is a significant activity. However, it is difficult only to obtain deterministic estimation since wind supplies are erratic and unpredictable. It increases learning that helps to project future values. Average wind speed is a major feature that affects the atmosphere. This paper explains the estimation of wind speed with ML algorithm. It is valuable for assessing the prospects for abnormal climate events and wind energy in the future.

Optimal power control in Renewable Energy sources using Intelligence algorithm

“… where ρ-air density, R-length of the blade, A - a swept area of the blade, λopt - tip ratio, CP - Power co-efficient, β - pitch angle. The maximum power extracted by WES using incremental conductance (INC) MPPT controller [ 24 , 25 ]. The WES specifications are given in Table 3 .…”

“…The Energy management system has been developed using the rule-based queuing algorithm in the design phase. ANFIS is a relatively new algorithm and it blends the utilization and exploratory phases of the search process and has shown the capacity to avoid remaining stuck in the local optimum [ [21] , [22] , [23] , [24] , [25] ]. In this paper author described in detail to reduce the effect of wind speed fluctuation on the power, speed regulation, and dynamic variation of load with a new control technique of intelligence algorithm named crow search algorithm tune by the adaptive model predictive control [ 26 ].…”