自适应序列生成的建筑能耗预测
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金(62072324, 61876217, 61876121, 61772357); 江苏省重点研发计划(BE2017663)


Prediction of Building Energy Consumption Generated by Adaptive Sequence
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    提出一种基于强化学习的生成对抗网络(Reinforcement learning-based Generative Adversarial Networks, Re-GAN)能耗预测方法. 该算法将强化学习与生成对抗网络相结合, 将GAN (Generative Adversarial Nets) 中的生成器以及判别器分别构建为强化学习中Agent (生成器)以及奖赏函数. 在训练过程中, 将当前的真实能耗序列作为Agent的输入状态, 构建一组固定长度的生成序列, 结合判别器及蒙特卡洛搜索方法进一步构建当前序列的奖赏函数, 并以此作为真实样本序列后续第一个能耗值的奖赏. 在此基础之上, 构建关于奖赏的目标函数, 并求解最优参数. 最后使用所提算法对唐宁街综合大楼公开的建筑能耗数据进行预测试验, 实验结果表明, 所提算法比多层感知机、门控循环神经网络和卷积神经网络具有更高的预测精度.

    Abstract:

    This study proposes an energy consumption prediction method based on Reinforcement learning and Generative Adversarial Networks (Re-GANs). The algorithm constructs the generator and discriminator in Generative?Adversarial?Nets (GANs) into the Agent and reward function in reinforcement learning respectively. In the training process, the current real energy consumption sequence is taken as the input state of the Agent (generator), and a set of generation sequences with a fixed length is constructed. Combined with the discriminator and Monte-Carlo search method, the reward function of the current sequence is further constructed as a reward for the first subsequent energy consumption value of the real sample sequence. On this basis, the objective function of reward is constructed, and the optimal parameters are solved. Finally, the proposed algorithm is used to predict the public building energy consumption data of the Downing Street complex. The experimental results show that the proposed algorithm has higher prediction accuracy than the multi-layer perception machine, gated loop neural network, and convolution neural network.

    参考文献
    相似文献
    引证文献
引用本文

王悦,陈建平,傅启明,吴宏杰,陆悠.自适应序列生成的建筑能耗预测.计算机系统应用,2021,30(11):155-163

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-01-25
  • 最后修改日期:2021-02-24
  • 录用日期:
  • 在线发布日期: 2021-10-22
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号