Abstract
Most current short-term load forecasting models have difficulty in simultaneously taking into account the time-series nature of load data, the non-linear characteristics, and the ineffectiveness of extracting potential high-dimensional features from historical series. To solve this problem, we propose a hybrid neural algorithm model (DPCL). In the DPCL, we use convolutional neural networks to obtain the high-dimensional spatial features of the phase space reconstruction of the load time series. Then, we combine the obtained high-dimensional spatial features with the external influence features extracted in the Pearson correlation analysis. Long and short-term memory networks retrieve Spatio-temporal features through the connection layer and obtain prediction results. In addition, there are problems of network gradient degradation and overfitting during the training process, We use an improved differential evolutionary algorithm to optimize the topology and time step of the hybrid neural network. We use the public dataset of a European utility and the loaded dataset of a Chinese mathematical competition as practical arithmetic examples. Experiments have higher prediction accuracy and faster prediction speed compared with other traditional algorithms.
Highlights
With the development of the economy, the power demand is constantly expanding[1]
Power dispatching is a crucial link for the safe operation of power grids, reliable external power supply, and orderly production of all kinds of electric power[2]
Load forecasting is the basis of power dispatching [3], which provides information and basis for the planning and construction of power grids and power sources as well as the operational decisions of power grid enterprises and power grid users, and is especially important for the economic and safe operation of power systems[4]
Summary
With the development of the economy, the power demand is constantly expanding[1]. Power dispatching is a crucial link for the safe operation of power grids, reliable external power supply, and orderly production of all kinds of electric power[2]. In the field of machine learning research, random forests [13], support vector machines [14], expert systems [15], artificial neural network forecasting methods [16], and deep learning methods [17] are commonly used for short-term load forecasting These methods offer a fast convergence of the algorithmic model through a simple internal structure, but VOLUME XX, 2017. The literature [25]-[26] proposes load forecasting based on the LSTM algorithm, combining CNN extracted features from different perspectives while using deep learning in both spatial and temporal domains to predict short-term load forecasts These researches have achieved good results, the models they used, especially the artificial neural network models, with excessive input data, implied layers, and implied layer nodes are likely to lead to overfitting, gradient disappearance, and the explosion of the network training, and the single neural network models generally exhibit shortcomings such as insufficient prediction accuracy. The above analysis shows that the inputs to the model are mainly the historical load and three characteristic factors of working days, temperature, and humidity
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