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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 shor...
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Citations
... Short-term wind power prediction in [41] explores correlation between wind speed and wind power data. This method combines NN and PLS to form Nonlinear Partial Least Square (NPLS) method. ...
With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses and a comparative analysis is presented. This study also provides discussions of recent development trends, performance analysis and future recommendations.
... In this paper, partial least squares regression (PLSR) [53] is used to reduce the prediction error from BO-LSTM. Given a linear model y � α + Xβ + ϵ, where X is the explanatory variable, y is the response, α and β are the regression parameters, and ϵ is the error term. ...
Wind energy is one of the fastest growing renewable energy sources. Wind speed forecasting is essential to enhance the utilization of wind energy. Various prediction models have been developed to improve the prediction accuracy of wind speed. However, wind speed time series has nonlinearity, fluctuation, and intermittence, which makes the prediction difficult. Existing prediction models ignore data decomposition and feature reduction and suffer from the deficiency of individual models. This paper proposes a novel ensemble prediction model, which integrates data preprocessing, feature selection, parameter optimization, three intelligent prediction models, and an ensemble strategy. To improve prediction performance, a highly efficient optimization algorithm is applied to determine the individual models’ optimal parameters. Furthermore, partial least square regression is used to calculate combination weight. Additionally, two 10 min datasets from the National Renewable Energy Laboratory (NREL) are employed for one-step-ahead prediction. Among the involved models, the proposed model can obtain the best prediction accuracy. The experimental results indicate that the mean absolute percent errors of the proposed model are 7.97% and 9.99%, which are lower than the comparison methods. Pearson’s test reveals that the proposed approach can have the strongest association between the actual data and the prediction results.
... However, the intermittency and uncertainty of wind make it a challenge to integrate wind power into the power system. 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]. ...
This paper develops an integrated machine learning and enhanced statistical approach for wind power interval forecasting. A time-series wind power forecasting model is formulated as the theoretical basis of our method. The proposed model takes into account two important characteristics of wind speed: the nonlinearity and the time-changing distribution. Based on the proposed model, six machine learning regression algorithms are employed to forecast the prediction interval of the wind power output. The six methods are tested using real wind speed data collected at a wind station in Australia. For wind speed forecasting, the long short-term memory (LSTM) network algorithm outperforms other five algorithms. In terms of the prediction interval, the five nonlinear algorithms show superior performances. The case studies demonstrate that combined with an appropriate nonlinear machine learning regression algorithm, the proposed methodology is effective in wind power interval forecasting.
... 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]. ...
... 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. ...
A novel energy management system to improve the efficiency of renewable energy and storage system by scheduling various types of household appliances is developed. The end users schedule appliances optimally taking into account maximum utility (a measure of the satisfaction level of user’s electricity consumption) as well as minimum user’s cost of energy as competitive objectives. A random disturbance (reflecting an uncertainty) is introduced to describe the indeterminate amount of the electricity produced from the renewable energy sources at the adjacent slots. By applying the probability theory, the uncertain optimization model is transformed into a convex optimization problem. Then, the optimal solution is obtained using a quasi-Newton method. The rationality of our proposed model is verified through numerical simulations. According to the results of simulation studies, it is demonstrated that our proposed model not only enhances users’ utility but also reduces energy consumption cost.
... The wind technology was gradually improved since the early 1970s. By the end of the 1990s, wind energy hasre-emerged as one of the most important renewable energy resources [4][5][6][7][8][9][10]. ...
Wind energy is one of the economic renewable sources and a valuable supplement to conventional energy sources. Small wind power generator can be used for substitution of conventional energy source. The main objective of the present study is to estimate small wind power potential using wind speed data. The result shows, the highest maximum wind power potential was found in Gifu city it was 4.77 watt. However, the total wind power potential at 2016-2017 the highest is 50365.62 Kwh in Gero city and the lowest was found in Gujo city with only 884.32 Kwh. The potential for wind power was strongly influenced by the geological condition and seasonal change. The high the potential of small wind power found in March-May, this time changes the season from winter to spring or spring. Gero city, Gifu city, and Takayama city have the potential to install small wind power generator, but not recommended for Gujo city and Nakatsugawa city.
