Adaptive IES Load Forecasting Method Based on the Octopus Model

Abstract

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Improving the accuracy and speed of integrated energy system load forecasting is a great significance for improving the real-time scheduling and optimized operation of the integrated energy system. In order to achieve rapid and accurate forecasting of the integrated energy system, this paper proposes an adaptive integrate energy system (IES) load forecasting method based on the octopus model. This method uses long short-term memory (LSTM), support vector machines (SVMs), restricted Boltzmann machines (RBMs), and Elman neural network as the octopus model quadrupeds. Through taking over differences in different data and training principles and utilizing the advantages of the octopus quadruped model, a special octopus-head and XGBoost algorithm were adopted to set the weight of the octopus’ quadruped and prevent local minimum points in the model. We train the octopus model through RMSProp adaptive learning algorithm, constrain the learning rate, get the best parameters, and improve the model’s adaptability to different types of data. In addition, for the incomplete comprehensive energy load data, the generative confrontation network is used to fill it. The simulation results show that compared with other prediction methods, the effectiveness and feasibility of the method proposed in this paper are verified.

Keywords

• comprehensive energy
• generative confrontation network
• XGBoost algorithm
• RMSProp adaptive learning
• octopus model