Load forecasting is the basis of economic operation of power system. In the time series forecasting method based on deep neural network, single load forecasting method can not meet the requirements of load forecasting in the new period. Therefore, a short-term power load forecasting method based on CNN-LSTM neural network is adopted in this paper. This paper first introduces the data set used and the pre-processing operation of the data sample. Then, a sequential hybrid model consisting of single-layer CNN and two-layer LSTM is designed. Finally, the experimental results obtained by using the hybrid model are compared with those obtained by using CNN alone and LSTM alone. The experimental results show that the power load prediction method proposed in this paper gives full play to the advantages of multiple models, effectively deals with the linear and nonlinear characteristics of data samples, and further improves the accuracy of load prediction.
In order to improve the short-term power load prediction results, this paper uses long short-term memory network to predict short-term power load. With the improvement of accuracy requirements, the traditional load forecasting does not consider problems such as time series and eigenvalues. This paper proposes a long short-term memory network model to predict load. First, the data set is processed, cleaned and normalized, and the data set is divided into training set and sample set. Then, a prediction model is built, and appropriate parameters and eigenvalues are selected for the model to study the impact on the short-term power load under the LSTM model. This paper uses the Short-term electricity load forecasting (Panama) dataset on the kaggle platform to verify the model, and uses multivariate and multistep to forecast short-term electricity load.
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