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Particulate Matters such as PM10, PM2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM10 and PM2.5. In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO2, NO, NO2, O3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models. [ABSTRACT FROM AUTHOR]

• 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]