Abstract
This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations that considers the temporal and spatial distribution of precipitation, which compensates for the shortcomings of traditional forecasting methods that do not consider differences in the spatial distribution of precipitation. First, the actual precipitation values measured by ground weather stations and the spatial distribution of precipitation observed by meteorological satellite remote sensing are used to complete the missing precipitation data through linear interpolation, and the gridded precipitation data covering a group of small hydropower stations are constructed. Then, considering the time lag between changes in the daily power generation of the group of small hydropower stations and precipitation, the partial mutual information method is used to estimate the “time difference” between the two, and combined with the precipitation grid data, a data set of the temporal and spatial distribution of precipitation is generated. Finally, using only the temporal and spatial distribution of precipitation and historical power generation data, a multimodal deep learning network based on a convolutional neural network (CNN) and multilayer perceptron (MLP) is constructed, and a highly accurate prediction model for the daily power generation of small hydropower stations is obtained. Taking the real power generation data of a group of small hydropower stations in southern China as an example, after considering the temporal and spatial distribution of precipitation, the prediction accuracy of the proposed method is as high as 93%, which is approximately 5.8% higher than before considering the temporal and spatial distribution of precipitation. In addition, compared with mainstream methods such as support vector regression (SVR) and the long–short-term memory network (LSTM) (the average accuracy is about 87%), and the average accuracy improvement of the proposed method is approximately 6%.
Highlights
As a world-recognized renewable and clean energy, the rational development and utilization of small hydropower are of great significance for China to achieve the goal of “peak carbon and carbon neutrality”
To fully explore the characteristic information contained in the temporal and spatial distribution of precipitation data and the general trend of recent and historical power generation, this paper proposes a multimodal deep learning network based on a convolutional neural network and multilayer perceptron (CM-MDLN)
This paper proposes a multimodal deep learning method for forecasting the daily power generation of small hydropower stations considering the temporal and spatial distribution of precipitation
Summary
As a world-recognized renewable and clean energy, the rational development and utilization of small hydropower are of great significance for China to achieve the goal of “peak carbon and carbon neutrality”. The key to improving the forecast accuracy of the power generation capacity of small hydropower stations lies in obtaining more abundant precipitation and historical operating data and adopting appropriate methods to fully extract the potential characteristic information. With the help of the precipitation distribution field obtained by meteorological satellite remote sensing observations and the partial mutual information method, the difference in the temporal and spatial distribution of precipitation is included in a forecast of the short-term power generation of small hydropower stations for the first time in this paper. To fully explore the characteristic information contained in the temporal and spatial distribution of precipitation data and the general trend of recent and historical power generation, this paper proposes a multimodal deep learning network based on a convolutional neural network and multilayer perceptron (CM-MDLN). With the screening of independent variables, the AIC value decreases continuously, and the screening ends when it reaches the minimum value, which means that the input variable set with the most significant correlation has been selected
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