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• This paper proposed a novel wind speed forecasting algorithm. • A new dataset division mechanism based on the moving window is used in this paper. • In order to improve the accuracy and stability simultaneously, the multi-objective particle swarm optimization is applied. • We compared the proposed MOPSO-Combined method with the combined model that published before in the paper. Accurate wind speed forecasting is important in power grid security, power system management, operation and market economics. However, most research has focused only on improving either accuracy or stability, with few studies addressing the two issues, simultaneously. Therefore, we proposed a novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network. Most combined wind speed forecasting methods just use the combination theory to combine individual methods, this paper uses echo state network to combine the intermediate wind speed forecasting results of three artificial neural networks. Moreover, a new dataset division mechanism based on the moving window is applied in this paper. Firstly, the length of the input data is changed from 5 to 15 for 1-step, 2-step and 3-step wind speed forecasting, after that, the optimal length of the input vector can be got. And then we apply this optimal length of the input vector to another dataset for further verifying the proposed method. In order to verify the forecasting effectiveness of the proposed forecasting model, the 80/min wind speed data of M2 tower of the National Wind Power Technology Center of the United States were taken as an example. The experimental results indicate that the proposed algorithm is superior to the other ten comparative models in prediction accuracy and stability, and it also performs better than the combined model that we have proposed before. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposed a novel combined model based on echo state network. • A new dataset division mechanism is applied in this paper. • ESN is initially applied to combine the intermediate forecasting results of four hybrid models. • The proposed model is tested comprehensively in different wind speed sites. Abstract Accurate wind speed forecasting plays a crucial role in wind power plants and wind systems. Numerous studies that dedicated to enhance wind speed forecasting accuracy have been proposed. Nevertheless, some classical models are not good at dealing with nonlinear time sequence, and artificial intelligence are easy fall into local optimum. Based on this, this paper proposed a novel combined model for wind speed forecasting, which combined hybrid models based on decomposition method and optimization algorithm. In this approach, four distinct hybrid models are proposed and to additionally enhance the forecasting performance, ESN (Echo state network) is initially applied to integrate all the results obtained by each hybrid model and achieve the ultimate forecasting results. Besides, a distinct data set division mechanism is adopted in the multi-step wind speed forecasting. To verify the effectiveness and versatility of the proposed combined model, the wind speed data of 20 m, 50 m, 80 m per minute of the M2 tower from the National Wind Power Technology Center, United States, are utilized as case studies. The experimental results indicate that the proposed combined model performs better than other conventional methods. [ABSTRACT FROM AUTHOR]

Wind energy has attracted much attention because it is sustainable and renewable energy with a much smaller impact on the environment than fossil fuels. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, many papers only use wind speed series but ignore weather factors, which lead to poor forecasting results. In this paper, we propose an ensemble system that considers historical wind speed series and other weather factors to make wind speed forecasting. The proposed system integrates noise reduction, clustering, and multi-objective optimization. Firstly, the historical wind speed series are decomposed by complementary ensemble empirical mode decomposition (CEEMD). Secondly, the samples are clustered by self-organizing map (SOM) according to the weather factors. Finally, a regularized extreme learning machine (RELM) model is trained to forecast the wind speed for each cluster, and multi-objective grey wolf optimizer (MOGWO) is employed to optimize the parameters in the model. An empirical study using three datasets from the M2 tower of the national wind power technology center (NWTC) of the national renewable energy laboratory (NREL) illustrates that the proposed system performs better than other comparative models in terms of four performance indicators. • The proposed system not only considers historical wind speed data, but also considers other weather factors. • SOM is successfully applied to cluster the samples, and the samples are clustered according to the weather factors. • Based on CEEMD and ACF, a new time series de-noised method is proposed. • Design four experiments based on actual wind farms to examine the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a significant role in economic and social development. As a challenging but indispensable task, STLF has become a hot topic in the field of energy. However, the inadequacy of existing methods lies in their inability to capture accurate input features that are highly related to the output, as the main focus of research has been on improving the accuracy of STLF, while ignoring its stability. Therefore, in this paper, a novel, robust hybrid forecasting system was developed, composed of four modules: (1) data preprocessing, (2) forecasting, (3) optimization, and (4) evaluation. In the data preprocessing module, an effective data preprocessing scheme based on singular spectrum analysis and gray correlation analysis was used to produce a smoother time series and to mine the best input and output structure for the model. An extreme learning machine (ELM) optimized using a multiobjective genetic algorithm (MOGA) that considers both the forecasting accuracy and stability was employed to provide the forecasting. Additionally, a generalized regression neural network (GRNN) was also used in the subsequent module to perform forecasting. Moreover, to further obtain accurate results and to overcome the drawbacks of using single models, a simulated annealing (SA) algorithm was utilized to optimize the combined parameters of the MOGA–ELM and GRNN algorithms in the optimization module. To validate the proposed model, half-hourly load data from the New South Wales and Tasmania are provided as illustrative cases. The experimental results show that the proposed hybrid model obtains more accurate and stable results than the reference models used for comparison. [ABSTRACT FROM AUTHOR]

More accurate and reliable wind speed forecasting results can provide an effective assessment of wind energy resources and improve the efficiency of wind energy utilization. Therefore, this paper is devoted to constructing accurate wind speed forecasting models and quantitatively analyzing the models by using interpretable analysis. Firstly, a multivariate wind speed forecasting model (PMI-CMOGSA-RELM) is proposed based on machine learning methods and a clustering-based multi-objective gravity search algorithm (CMOGSA). Among them, wavelet packet decomposition (WPD) is used for noise reduction, candidate input feature pool and partial mutual information (PMI) are used for feature selection, and CMOGSA is used to optimize the final combined weights. Secondly, a new evaluation metric, Absolute Error Coverage Probability (AECP), is proposed to better evaluate forecasting accuracy. Then, post-hoc attribution analysis methods and visualization tools are used to analyze the forecasting model interpretability to help us better analyze the robustness and reliability of the forecasted results. Finally, this paper validates and evaluates the proposed model with two data sets of different resolutions. The experimental results not only prove the rationality of the AECP evaluation criteria, but also demonstrate that the proposed model has smaller errors, higher estimation accuracy, and is easier to understand. • The proposed model is based on multiple time-lag multivariate data. • In this paper, CMOGSA is proposed based on clustering algorithm. • A new evaluation metric is proposed in this paper. • Interpretability is analyzed from significance and sensitivity of model weight. • PDP and ICE visualization analyses are used for interpretable studies. [ABSTRACT FROM AUTHOR]

Electricity, a kind of clean energy, has been widely used in people's production and daily life. However, it is very difficult to estimate the electricity energy production in advance and store the rest of the electric energy due to the climate, environment, population and other factors. Based on data preprocessing and artificial intelligence optimization algorithm, this paper introduces a combined forecasting method. The proposed method contains six individual methods, and each individual method has its own usage. Singular spectrum analysis (SSA) is adopted to reduce noise from the original data; three individual forecasting methods, Jordan neural network, the echo state network, least squares support vector machine, are applied to obtain the intermediate forecasting results; two optimization algorithms, particle swarm optimization and simulated annealing, are used to optimize the parameters of the combined model. This paper not only validates the superiority of the combined model compared to the single predictive model through the simulation experiments of power load data and electricity price data. The mean absolute percent error (MAPE) of the combined power load and electricity price forecast results are 1.14% and 7.58%, respectively, which are higher than the MAPE error of the corresponding single models prediction results. It has also been verified that the process of eliminating noise by the SSA plays a positive role in the accuracy of the combined forecasting model. In addition, two series of experiments on the power load data lead to two very interesting conclusions. One of the conclusions is that as the size of the test data increases, the prediction accuracy of the model decreases; the other is that the predicted result calculated through the optimized combined weight is better than the combined result calculated using the average weight, and the average weight is used. Weighted combination does not improve the prediction accuracy of a single model. [ABSTRACT FROM AUTHOR]