Short-term power load prediction method of improved CNN-LSTM algorithm

The invention relates to a short-term power load prediction method based on an improved CNN-LSTM algorithm, and the method comprises the steps: obtaining multi-dimensional input data of a power system, extracting main power load factors through a principal component analysis method, and obtaining power load data; processing the power load data by adopting a method of combining orthogonal wavelet decomposition and Kalman filtering, and eliminating the volatility of the power load data and individual data abrupt change points; inputting the processed power load data into a CNN-LSTM model to obtain a short-term power load prediction result; in the training process of the CNN-LSTM model, an improved particle swarm optimization algorithm is adopted to optimize hyper-parameters of the model, the improved particle swarm optimization algorithm is provided with a random inertia weight and a self-adaptive learning factor, the random inertia weight enables the weight to be evolved towards an expected weight direction, a