Wind power generation probabilistic modeling using ensemble learning techniques

Abstract

Abstract The generation and allocation of sustainable electrical energy in environment friendly, reasonable and socially acceptable manner is the utmost challenge. The primary objective is apparently a transition into an energy system to reduce fossil fuel dependency considering the global increasing energy demand, the scarcity of fossil fuel, and the serious environmental consequences of these fuels. The introduction of renewable energy in the power network is therefore essential. Wind power is one of the world’s most extensively utilized sources of energy. The generation of wind power varies from traditional methods of generating electricity because, of the probabilistic nature of wind. Therefore, in view of the instability of wind energy production, wind power forecasts play an important role in addressing the complexities of balance supply and demand in any power system. An accurate wind speed forecasts minimize the necessity of auxiliary energy balancing and reserve power to incorporate wind energy. This paper presents an accurate wind speed and wind power prediction methodology using ensemble machine learning algorithms.