论文标题
基于人工神经网络的负载预测，基于粒子群优化的需求响应
Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction
论文作者
论文摘要
在本研究中，提出了基于人工神经网络（ANN）预测负载的基于粒子群优化（PSO）的需求响应（DR）模型。德克萨斯州奥斯汀市的住宅区的电气负载和气候数据被用作ANN的输入。然后，使用日前价格数据的结果用于解决负载转移和降低成本问题。根据结果，提议的模型具有降低付款成本和峰值负载的能力。
In the present study, a Particle Swarm Optimization (PSO) based Demand Response (DR) model, using Artificial Neural Network (ANN) to predict load is proposed. The electrical load and climatological data of a residential area in Austin city in Texas are used as the inputs of the ANN. Then, the outcomes with the day-ahead prices data are used to solve the load shifting and cost reduction problem. According to the results, the proposed model has the ability to decrease payment costs and peak load.
