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Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.

Detecting and diagnosing faults that degrade the performance of heating, ventilation, and air conditioning (HVAC) systems is very important for maintaining high energy efficiency. The performance of HVAC systems can be evaluated by analyzing monitored data. However, data from a HVAC system generally includes uncertainties, which renders monitored data less reliable. Then, we focused on uncertainties and a calculated performance distribution. The uncertainties from sensors, actuators, and communications were modelled stochastically and were incorporated into a detailed simulation. The system coefficient of performance (SCOP) was used as a performance indicator, which is defined as the ratio of suppled heat to total power consumption. The SCOP distributions over the course of representative weeks in 2007 and 2015 were calculated by repeating the simulation 2,000 times with different uncertainties. Regarding the results for 2015, the 90% confidence interval of the distribution was -4.9% to 5.8% from the SCOP value without uncertainties. The SCOP value determined from the monitored data in 2015 was outside of the low end of the distribution though that in 2007 was inside of the interval. Through an analysis of the monitored data, it was found that fault detection is possible by comparing the monitored data with the distribution.

The energy management of heating, ventilating, and air-conditioning (HVAC) system is a primary concern in building projects. Although they allow us to identify opportunities for effective energy use, their⁠ cost can sometimes exceed the energy saved. On the contrary, controller tuning by e.g. set-point regulation, often provides solutions that incur relatively low or no extra cost. In this paper, a simulation-based optimization approach is proposed for the optimal set-point regulation of HVAC system. For this purpose, we developed dynamic models for the target HVAC system that can simulate system behaviour at intervals of one minute. The set-point values were optimized while assessing controllability. The optimization of set-points that considered only energy efficiency led to various faults and therefore, was not applicable. However, optimal set-points obtained by considering controllability can help avoid significant faults. [ABSTRACT FROM AUTHOR]

Faults that impair performance can occur in a heat source system because it comprises various devices and has complex controls. This article presents a novel method for fault detection and diagnosis (FDD). This study focused on a real system with a water thermal storage tank. First, system behaviors in response to faults were determined using a detailed system simulation. Then, a fault database was generated using the simulation results with fault labels. We preprocessed the database and converted the data into images. Then, convolutional neural networks (CNNs) were trained using the database, and the trained CNNs were used for diagnosing real data. The accuracy of the CNNs was 98.7% in training, and real data were diagnosed with probabilities. We analyzed the real data, where the probability indicated the likely presence of a fault and reviewed how the real data were similar to the fault assumed in the simulation. We concluded that the proposed FDD method will help in analyzing real data, as it indicates faults emerging in the real data with probability, whereas conventional data analysis requires checking the data using expert knowledge. [ABSTRACT FROM AUTHOR]