• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Smart Cities Conference >A Neural Network-based Model Predictive Control Approach for Buildings Comfort Management
【24h】

A Neural Network-based Model Predictive Control Approach for Buildings Comfort Management

机译：基于神经网络的建筑物舒适度模型预测控制方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a model predictive control (MPC) approach incorporated with machine learning to control the energy consumption and occupants’ comfort (thermal and visual comfort) in a smart building. Neural networks (NN)s are developed to learn and predict the building’s comfort specifications, environmental conditions, and power consumption. Based on the predicted data, MPC provides optimal control inputs for the thermal and lighting systems to achieve the desired performance. In contrast to the existing building control frameworks, our proposed learning-based control method incorporates the occupant-related parameters in the control loop, which enhances the prediction accuracy and control performance. Our proposed learning-based MPC approach is implemented on a building, simulated in EnergyPlus software, and its performance is compared with that of a model-based building control framework. From the simulation results, our control method performs significantly better than the conventional MPC in maintaining residents’ comfort and reducing energy consumption.
机译：本文提出了一种模型预测控制（MPC）方法，该方法与机器学习相结合，可以控制智能建筑中的能耗和居住者的舒适度（热舒适度和视觉舒适度）。开发了神经网络（NN），以学习和预测建筑物的舒适度规范，环境条件和功耗。基于预测的数据，MPC为热和照明系统提供最佳控制输入，以实现所需的性能。与现有的建筑物控制框架相比，我们提出的基于学习的控制方法在控制回路中纳入了与乘员相关的参数，从而提高了预测的准确性和控制性能。我们提议的基于学习的MPC方法是在建筑物上实施的，并在EnergyPlus软件中进行了仿真，并将其性能与基于模型的建筑物控制框架的性能进行了比较。从仿真结果来看，我们的控制方法在保持居民的舒适度和减少能耗方面比传统的MPC显着更好。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号