Hybrid Ventilation System and Soft-Sensors for Maintaining Indoor Air Quality and Thermal Comfort in Buildings

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors

Semantic segmentation of longitudinal thermal images for identification of hot and cool spots in urban areas

This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to collect thermal images of different types of buildings on the educational campus of the National University of Singapore. A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features such as buildings, vegetation, sky, and roads. It was observed that the U-Net segmentation model with `resnet34' CNN backbone has the highest mIoU score of 0.99 on the test dataset, compared to other models such as DeepLabV3, DeeplabV3+, FPN, and PSPnet. The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features. Further, various statistical measure of the temperature extracted using the predicted segmentation masks is shown to closely match the temperature extracted using the ground truth masks. Finally, the masks were used to identify hot and cool spots in the urban feature at various instances of time. This forms one of the very few studies demonstrating the automated analysis of thermal images, which can be of potential use to urban planners for devising mitigation strategies for reducing the urban heat island (UHI) effect, improving building energy efficiency, and maximizing outdoor thermal comfort.Comment: 14 pages, 13 figure

Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterization

This work presents a study on the characterization of the air-conditioning (AC) usage pattern of non-residential buildings from thermal images collected from an urban-scale infrared (IR) observatory. To achieve this first, an image processing scheme, for cleaning and extraction of the temperature time series from the thermal images is implemented. To test the accuracy of the thermal measurements using IR camera, the extracted temperature is compared against the ground truth surface temperature measurements. It is observed that the detrended thermal measurements match well with the ground truth surface temperature measurements. Subsequently, the operational pattern of the water-cooled systems and window AC units are extracted from the analysis of the thermal signature. It is observed that for the water-cooled system, the difference between the rate of change of the window and wall can be used to extract the operational pattern. While, in the case of the window AC units, wavelet transform of the AC unit temperature is used to extract the frequency and time domain information of the AC unit operation. The results of the analysis are compared against the indoor temperature sensors installed in the office spaces of the building. It is realized that the accuracy in the prediction of the operational pattern is highest between 8 pm to 10 am, and it reduces during the day because of solar radiation and high daytime temperature. Subsequently, a characterization study is conducted for eight window/split AC units from the thermal image collected during the nighttime. This forms one of the first studies on the operational behavior of HVAC systems for non-residential buildings using the longitudinal thermal imaging technique. The output from this study can be used to better understand the operational and occupant behavior, without requiring to deploy a large array of sensors in the building space

BEEM: Data-driven building energy benchmarking for Singapore

10.1016/j.enbuild.2022.111869ENERGY AND BUILDINGS26010.1016/j.enbuild.2022.11186

Islands of misfit buildings: Detecting uncharacteristic electricity use behavior using load shape clustering

10.1007/s12273-020-0626-1BUILDING SIMULATION141119-13

EnergyStar plus plus : Towards more accurate and explanatory building energy benchmarking

10.1016/j.apenergy.2020.115413APPLIED ENERGY27

OpenBAN: An Open Building ANalytics Middleware for Smart Buildings

Towards the realization of smart building applications, buildings are increasingly instrumented with diverse sensors and actuators. These sensors generate large volumes of data which can be analyzed for optimizing building operations. Many building energy management tasks such as energy forecasting, disaggregation, among others require complex analytics leveraging collected sensor data. While several standalone and cloud-based systems for archiving, sharing and visualizing sensor data have emerged, their support for analyzing sensor data streams is primitive and limited to rule-based actions based on thresholds and simple aggregation functions. We develop OpenBAN, an open source sensor data analytics middleware for buildings, to make analytics an integral component of modern smart building applications. OpenBAN provides a framework of extensible sensor data processing elements for identifying various building context, which different applications can leverage. We validate the capabilities of OpenBAN by developing three representative real-world applications which are deployed in our test-bed buildings: (i) household energy disaggregation, (ii) detection of sprinkler usage from water meter data, and (iii) electricity demand forecasting. We also provide a preliminary system performance of OpenBAN when deployed in the cloud and locally

Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset

10.1016/j.apenergy.2018.12.025APPLIED ENERGY2361280-129