scholarly journals A Combined Forecasting System Based on Modified Multi-Objective Optimization for Short-Term Wind Speed and Wind Power Forecasting

2021 ◽  
Vol 11 (20) ◽  
pp. 9383
Author(s):  
Qingguo Zhou ◽  
Qingquan Lv ◽  
Gaofeng Zhang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Processing Strategy ◽  
Multi Objective Optimization ◽  
Combined Model ◽  
Multi Objective ◽  
Forecasting System ◽  
Power Forecasting

Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.

scholarly journals Ensemble Forecasting Frame Based on Deep Learning and Multi-Objective Optimization for Planning Solar Energy Management: A Case Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Yongjiu Liu ◽  
Li Li ◽  
Shenglin Zhou
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Strategies ◽  
High Efficiency ◽  
Prediction Models ◽  
Ensemble Forecasting ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Photovoltaic Power ◽  
Power Forecasting

There are many prediction models that have been adopted to predict uncertain and non-linear photovoltaic power time series. Nonetheless, most models neglected the validity of data preprocessing and ensemble learning strategies, which leads to low forecasting precision and low stability of photovoltaic power. To effectively enhance photovoltaic power forecasting accuracy and stability, an ensemble forecasting frame based on the data pretreatment technology, multi-objective optimization algorithm, statistical method, and deep learning methods is developed. The proposed forecasting frame successfully integrates the advantages of multiple algorithms and validly depict the linear and nonlinear characteristic of photovoltaic power time series, which is conductive to achieving accurate and stable photovoltaic power forecasting results. Three datasets of 15-min photovoltaic power output data obtained from different time periods in Belgium were employed to verify the validity of the proposed system. The simulation results prove that the proposed forecasting frame positively surpasses all comparative hybrid models, ensemble models, and classical models in terms of prediction accuracy and stabilization. For one-, two-, and three-step predictions, the MAPE values obtained from the proposed frame were less than 2, 3, and 5%, respectively. Discussion results also verify that the proposed forecasting frame is obviously different from other comparative models, and is more stable and high-efficiency. Thus, the proposed frame is highly serviceable in elevating photovoltaic power forecasting performance and can be used as an efficient instrument for intelligent grid programming.

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