.png){kind=icon}
返回
Load pattern recognition based optimization method for energy flexibility in office buildings
.png){kind=icon}
PRE
AI
DOI：10.1016/j.energy.2022.124475.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Air conditioning systems are generally considered to have the greatest flexibility potential in buildings that can be flexibly regulated with thermal storage to reduce the interaction with the power grid and increase demand response benefits. In previous studies, the flexibility of air-conditioning systems was reflected through time-of-use tariffs. However, a strategy that only factors the tariffs incurs a greater operational energy consumption. In this study, a flexibility factor was established and incorporated into the multi-objective optimization process, together with the operational energy consumption, as two optimization objectives. After obtaining typical load patterns using a two-step clustering method, for multi-objective decision-making in the day-ahead operation, the entropy-grey technique for order preference by similarity to an ideal solution method is used. Considering an office building as a case study, we found that the optimized flexibility factor can reach 0.31 and 0.99 during a week of operation in winter and summer, on average, respectively, and achieved a cumulative energy-saving effect of 17.98% and 35.49%. In addition, the two-step clustering method can better demonstrate the flexibility factor than the single-step clustering.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Two-step clustering
Load pattern recognition
Flexibility factor
Operation strategy
Table 1
期刊
IF:
9.4
论文数:
4.3W
被引数:
20.2W
机构
引用论文
State of the art in building modelling and energy performances prediction: A review建筑建模和能源性能预测的最新技术: 综述
OAAI
OAAI分享收藏Data-driven predictive control for unlocking building energy flexibility: A review数据驱动的预测控制，用于释放建筑能源灵活性: 综述OAAI
OAAI分享收藏GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Cluster analysis of residential heat load profiles and the role of technical and household characteristics住宅热负荷曲线的聚类分析以及技术和家庭特征的作用ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Economic model predictive control for demand flexibility of a residential building住宅建筑需求灵活性的经济模型预测控制ENERGYOAAI
ENERGYIF9.4
OAAI分享收藏Experimental and numerical analysis of hybrid solar heating and cooling system for a residential userRENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏Multi-objective optimisation approach for campus energy plant operation based on building heating load scenariosAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容