Abstract
The multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. Firstly, the multienergy information coupling correlation of the regional integrated energy system is analyzed, and the horizontal interaction and vertical interaction mode are proposed. Then, based on the long short-term memory depth neural network time series prediction, parallel long short-term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user-driven behavioral data to achieve vertical interaction between source and load. Finally, uncertain resources composed of wind power, photovoltaic, and various loads on both sides of source and load integrated energy prediction are achieved. The simulation results of the measured data show that the interactive parallel prediction method proposed in this article can effectively improve the prediction effect of each subtask.
Highlights
With the depletion of fossil energy, the contradiction between energy demand growth and energy shortage in the process of social and economic development, energy utilization, and environmental protection is becoming more and more serious
Inspired by the above literature, this paper argues that it is necessary to explore the value of multienergy coupling information and load-side user behavior characteristics for energy system operation and use the information correlation characteristics between multiple energy sources to expand the concept of multienergy interaction to the forecasting stage. e “information interaction” strategy is proposed and explored the data information implied in each link of regional integrated energy system (RIES) by combining deep learning technology, and the energy utilization efficiency is maximized through multienergy interaction
For an RIES, the uncertainties of the source-side wind, light, and other uncertain resources are large and have a great influence on the stable operation of the system and the energy dispatch management. erefore, the source-side wind forecasting is set to a higher weight; in addition, as the power grid plays a leading role in the integrated energy system by virtue of its perfect architecture and central hub advantages, correspondingly, the power load forecast is set to a higher weight
Summary
With the depletion of fossil energy, the contradiction between energy demand growth and energy shortage in the process of social and economic development, energy utilization, and environmental protection is becoming more and more serious. Liu et al [12] combined variational mode decomposition (VMD), singular spectrum analysis (SSA), long short-term memory (LSTM) network, and extreme learning machine (LEM) and proposed a new multistep wind speed prediction model. On the basis of multiple long short-term memory (LSTM) learning, the literature [13] combines the extreme value optimization algorithm and the support vector machine model to integrate the LSTM layer prediction results and predicts the wind speed in a short time. En, from the perspective of information interaction, based on the LSTM deep neural network time series prediction, the parallel LSTM multitask learning model is established to realize the horizontal interaction between multienergy systems. Based on the measured data of a region’s RIES, the prediction model is trained and verified. e simulation results show that the proposed information interactive parallel prediction method can effectively improve the prediction effect of each subtask
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