基于随机森林和长短期记忆网络多元负荷预测的综合能源三层规划调度

针对综合能源系统负荷不确定性对规划和调度造成的高成本低效率问题，提出一种基于多元负荷预测的3层规划调度模型，主要包括预测层、规划层和调度层；基于随机森林回归网络和长短期记忆网络构建了多元负荷的长期和短期预测模型；以综合规划调度成本和调度运行成本最小为目标，采用改进粒子群算法和CPLEX求解器获取最优系统综合成本及配置方案；通过不同场景下的规划调度，分析了设备状态与系统成本。通过对比所构建的3层模型与常规双层模型的规划调度结果,证明了3层规划调度模型的经济性与可靠性。

Three-level Planning and Scheduling of Comprehensive Energy Based on Random Forest R egression-Long- and Short-Term Memory Network Multivariate Load Forecasting

In allusion to the problem of high cost and low efficiency in power planning and dispatching due to load uncertainty in integrated energy system, a multivariate load forecasting-based three layer planning and dispatching model, in which the forecasting layer, the planning layer and the scheduling layer were included, was proposed. Based on random forest regression network and long- and short-term memory network a long-term and short-term multiple load prediction model was constructed. Taking the minimum integrated planning and dispatching cost and the minimum dispatching and operation cost as objectives, adopting improved particle swarm optimization algorithm and CPLEX solver, the optimal system comprehensive cost and allocation scheme was obtained. By means of planning and dispatching under different scenarios the equipment status and system cost were analyzed. Comparing the planning and dispatching results obtained by the constructed three layer model with that obtained by conventional two layer model, it is proved that the three layer planning and dispatching model possesses better economy and reliability.