Heat Balance Calculation and Energy Efficiency Analysis for Building Clusters Based on Psychrometric Chart

How to perform accurate calculation of heat balance and quantitative analysis of energy efficiency for building clusters is an urgent problem to be solved to reduce building energy consumption and improve energy utilization efficiency. This article proposes a method for the heat balance calculation and energy efficiency analysis of building clusters based on enthalpy and humidity diagrams and applies it to the energy management of building clusters containing primary return air systems and heating pipe networks. Firstly, the basic structure and energy management principle of building clusters with a primary return air system and a heating pipe network were given, and the heat balance calculation and energy efficiency analysis method based on i-d diagram was proposed to realize the accurate calculation of heat load and the quantification of energy utilization. Secondly, the energy management model of the building cluster with a primary return air system and a heating pipe network was established to efficiently manage the indoor temperature and the heating schedule of ASHP, HN and HI. Finally, the proposed method was validated by calculation examples, and the results showed that the proposed method is beneficial for improving the energy economy and energy efficiency of building clusters.

Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning

The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.