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The instability of solar energy is the biggest challenge to its successful integration with modern power grids, and accurate prediction of long‐term solar radiation can effectively solve this problem. In this study, we proposed a novel long‐term solar radiation prediction model based on time series imaging and bidirectional long short‐term memory network. First, inspired by the computer vision algorithm, the recursive graph algorithm is used to transform the one‐dimensional time series into two‐dimensional images, and then convolutional neural network is used to extract the features from the images, thus, the deeper features in the original solar radiation data can be mined. Second, to solve the problem of low accuracy of long‐term solar radiation prediction, a hybrid model BiLSTM‐Transformer is used to predict long‐term solar radiation. The hybrid prediction model can capture the long‐term dependencies, thereby further improving the accuracy of the prediction model. The experimental results show that the hybrid model proposed in this study is superior to other single models and hybrid models in long‐term solar radiation prediction accuracy. The accuracy and stability of the hybrid model are verified by many tests. [ABSTRACT FROM AUTHOR]

The wind power generation capacity is increasing rapidly every year. There needs to be a corresponding development in the management of wind power. Accurate wind speed forecasting is essential for a wind power management system. However, it is not easy to forecast wind speed precisely since wind speed time series data are usually nonlinear and fluctuant. This paper proposes a novel combined wind speed forecasting model that based on PSR (phase space reconstruction), NNCT (no negative constraint theory) and a novel GPSOGA (a hybrid optimization algorithm that combines global elite opposition-based learning strategy, particle swarm optimization and the genetic algorithm) optimization algorithm. SSA (singular spectrum analysis) is firstly applied to decompose the original wind speed time series into IMFs (intrinsic mode functions). Then, PSR is employed to reconstruct the intrinsic mode functions into input and output vectors of the forecasting model. A combined forecasting model is proposed that contains a CBP (cascade back propagation network), RNN (recurrent neural network), GRU (gated recurrent unit), and CNNRNN (convolutional neural network combined with recurrent neural network). The NNCT strategy is used to combine the output of the four predictors, and a new optimization algorithm is proposed to find the optimal combination parameters. In order to validate the performance of the proposed algorithm, we compare the forecasting results of the proposed algorithm with different models on four datasets. The experimental results demonstrate that the forecasting performance of the proposed algorithm is better than other comparison models in terms of different indicators. The DM (Diebold–Mariano) test, Akaike's information criterion and the Nash–Sutcliffe efficiency coefficient confirm that the proposed algorithm outperforms the comparison models. [ABSTRACT FROM AUTHOR]

The use of wind power generation can reduce the pollution in the environment and solve the problem of power shortages on offshore islands, grasslands, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines on large wind farms. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method with the original wind speed dataset for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-term Memory (LSTM) neural network, are applied for wind speed forecasting. In addition, the variance reciprocal method and social cognitive optimization (SCO) algorithm are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20 m, 50 m and 80 m) at the National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights. [ABSTRACT FROM AUTHOR]

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]

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]

Particulate Matters such as PM 10 , PM 2.5 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 PM 10 and PM 2.5 . 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 SO 2 , NO, NO 2 , O 3 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.