The energy Internet (EI) is an important infrastructure for effectively utilizing and intelligently managing renewable energy sources (RES). In this paper, we study the architecture design of the EI under the backdrop of large-scale RES grid connection and the efficient forecasting and optimal utilization of energy. The contribution of this paper is threefold. First, we design a hierarchical integration architecture for the EI and attempt to solve the issues of energy and information management that stem from large-scale RES grid connection. Second, we propose a novel energy forecasting scheme that significantly reduces the amount of effort and ensures the accuracy of formulating the energy forecasting as an instance of the matrix completion issue. Third, we take electric vehicle charging as a typical case and propose the use of reinforcement learning to achieve optimal energy delivery. An experimental evaluation of real-world data sets validates the expectations of the study and highlights the superiorities of our proposed approaches. Energy internet, energy forecasting and delivery, matrix completion, reinforcement learning.
In REF , we designed a management architecture for the bottom renewable energy unit, and a hierarchically integrated EI architecture was proposed to provide reliable and efficient dual-flow management.
Efficient Forecasting Scheme and Optimal Delivery Approach of Energy for the Energy Internet
IEEE Access
IEEE Access
Computer Science