Wind Power Prediction Based on Nonlinear Partial Least Square

Abstract

Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical penetration of wind energy. The intermittent and the randomness of wind would affect the accuracy of prediction. According to the sequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction. The proposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the nonlinear partial least square is adopted to map the wind speed under the time series to wind power. The model carries the neural network as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of the original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the real data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation neural network, radial basis function neural network, support vector machine, and partial least square are performed on the real data and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the real power running curves also verify that the proposed method can predict the wind power in short-term effectively.

Demand Side Management for Smart Grid Based on Smart Home Appliances with Renewable Energy Sources and an Energy Storage System

“…, m) is a second-order continuous differentiable function on R n . e following aggregate function is a smooth approximation function of formula (18) [47]:…”

“…Scheduling the RES and ESS, power grids are becoming more secure and efficient in the electricity market [10,[14][15][16][17][18][19][20][21][22].…”

“…Research on the management of RES and ESS are at their infancy [23][24][25][26]. Moreover, in current studies, RES and ESS have mainly focused on the foundation level of load prediction [10,16,18,19,[27][28][29][30][31][32][33]]. An optimization method [10] based on teaching-learning and adaptive neuro-fuzzy inference is used to estimate the maximum actual capacity of a charging state.…”

“…In the other scenario, the impacts of the RES and ESS by manipulating semielastic and elastic appliances are studied under the appropriate range of ω k i in various models. [1,3,5,7,9,11] and [18,20], respectively. "w � 2" means ω k i ∈ [1,3] for the lowest satisfaction level; "w � 6" and "w � 10" mean ω k i ∈ [5,7] and ω k i ∈ [9,11] for the normal satisfaction levels, respectively; and "w � 20" means ω k i ∈ [19,21] for the highest satisfaction level.…”

Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm

“…e wind power forecasting system can greatly help the integration process since system operators rely on accurate wind power forecasts to design operational plans and assess system security [1,2]. Servo mechanism is the foundation of wind turbines, and precise wind power forecasting can improve the accuracy of parameter estimation and control of wind turbine servo systems [3][4][5].…”