Abstract

In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems. One way to optimize their usage is to make them demand-driven depending on human occupancy. This paper focuses on accurately estimating the number of occupants in a room by leveraging multiple heterogeneous sensor nodes and machine learning models. For this purpose, low-cost and non-intrusive sensors such as CO 2 , temperature, illumination, sound and motion were used. The sensor nodes were deployed in a room in a star configuration and measurements were recorded for a period of four days. A regression based method is proposed for calculating the slope of CO 2 , a new feature derived from real-time CO 2 values. Supervised learning algorithms such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM) and random forest (RF) were used on several different combinations of feature sets. Moreover, multiple performance metrics such as accuracy, F1 score and confusion matrix were used to evaluate the performance of our models. Experimental results demonstrate a maximum accuracy of 98.4% and a high F1 score of 0.953 for estimating the number of occupants in the room. Principal component analysis (PCA) was also applied to evaluate the performance of a dataset with reduced dimensionality.

Abstract

This study demonstrates an experimental example of application of time-lapse high dynamic range images for analyzing variations in correlated color temperature (CCT) and circadian lighting in an interior space daylit using a tubular daylight device (TDD). Previous studies have shown that the spectral composition of daylight affects human circadian rhythms, with emphasis on wavelengths towards the blue region of the visible spectrum. Spectrally based metrics such as CCT can therefore aid in evaluating the circadian impact of daylight. TDDs redirect daylight towards interior spaces with changing light levels and color temperatures during the daylight hours. This paper details the color temperature and circadian lighting metrics (circadian luminance and illuminance) in an interior space throughout a period of several days in a mid-latitude, temperate, sunny climate. An automated timelapse HDR photography apparatus was modified to allow for calculation of CCT and circadian lighting metrics using state-of-the art techniques available in the literature. Prior to the measurements in the space with the TDD, this device was calibrated against a spectrophotometer. Results indicate the level of daily variation in CCT and circadian lighting metrics that can be expected in interior spaces daylit by TDDs without any other sources of daylight. Besides showcasing the use of HDR techniques, these results could be used to support design decisions involving daylighting in interior spaces. These techniques can also be used outside the laboratory in the evaluation of the circadian impact of lighting throughout the built environment.

Abstract

A building is a boundary between an outdoor climate and indoor conditions. The building energy consumption, notably the energy used for air-conditioning is closely related to outdoor climate. The Solar Radiation, Drybulb Temperature and Relative Humidity are the important climatic parameters which affect building’s thermal and visual conditions. The energy codes development analyses and evaluation of new technologies with respect to energy savings and financial viability have been carried out for selected cities that are supposed to represent any particular climate zone. There is a significant change in thermal loads in different cities of a climate zone. Selection of a representative city for a climatic zone is important because it needs to reflect average climatic conditions for a particular climate zone accurately. This study presents a new method that has been developed for the selection of a representative city in any climate zone, using the principle of Euclidean distance approach having a dry-bulb temperature, wet bulb temperature and solar radiation as three axes. The city having minimum Euclidean distance with respect to remaining cities of the same climatic zone is proposed to be taken as a representative city. The proposed method has been applied on each of the five existing climatic zones of India according to National Building Code (NBC) of India and eight climate zones according to ASHRAE Standard 169-2013.

Abstract

Automated shading and integrated lighting control systems are being used in buildings for electrical energy savings and improve occupant’s comfort. These systems facilitate efective utilization of useful daylight in interior spaces that beneft an occupant’s health, well-being, and productivity by preventing glare and overheating while maintaining the adequate illuminance levels. The pragmatic procedure to implement open loop shading and integrated lighting control comprises three parts: (i) a reliable estimation of sky conditions on realtime basis, (ii) determining indoor daylight metrics using outdoor conditions, and (iii) incorporating the metrics in the blind and integrated lighting control techniques. This paper presents a review of experimental studies done on openloop window shade and integrated lighting control strategies. The aim of this paper is to analyse the performance and feasibility of various daylight prediction methods and their application in controlling blinds and integrated lighting system. The review mainly focuses on simulation assisted open loop control techniques that employ real-time daylight estimation methods. The review identifes the current challenges, recommends areas for improvement, and provides signifcant scope for future research. The paper concludes that modifed and improved open loop system are more competent as an alternative compared to the conventional methods for automated blind and lighting control systems. Advanced open-loop control systems along with calibrated simulations have the capability to reduce post commissioning errors, allow easy monitoring of the system and predict daylight more extensively.

Abstract

Internationally, the cooling degree-days (CDD) and heating degree-days (HDD) have been commonly used as indicators of cooling and heating load occurring due to outdoor temperature. These indicators have also applied for classification of climate zones as referred in ASHRAE Standard 169-2013. As of now various base temperatures has been used in the different part of the world. Additionally, the analysis for base temperature had been done in 1980 by ASHRAE. This study aims to determine the base temperature for cities in India. The simulation approach has been used to identify base temperature using Energy Signature and Performance line method for 60 cities of India. The US-DOE developed 16 reference buildings which have been used for eight climate zones of India. These eight climate zones have been classified using the methodology of ASHRAE Standard 169–2013 on weather data of Indian cities. It was observed that the cooling base temperature is varying from 6.8 °C to 28.6 °C for the large hotel and 8.7–28.1 °C for office buildings while the heating base temperature is ranging from 13.8 °C to 21.4 °C for the large hotel and 10.4–18.7 °C respectively for different cities. The estimated base temperature for cooling and heating is 18 °C for India.

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the Conventional system, but the main drawback of this technology is condensation and inability to cater the latent load. These challenges have directed many researchers to move their emphasis towards Personalized Conditioning System which aims to generate a microclimate zone near the vicinity of occupant according to need. This study is focused on Personalized Conditioning System when it is combined with a radiant system. The objectives of this study are 1) Evaluate the performance analysis of the system in cooling mode and, 2) Evaluate the performance of PRCS for cooling mode on different radiant cooling systems. An experimental setup of an office cubicle has been designed and fabricated using radiant panels at MNIT Jaipur, India. Three cases of the experiment were designed to evaluate and compare the performance of the system which are: Only Radiant Cooled Ceiling System (RCCS), Only Personalized Radiant Cooling System (PRCS), and both PRCS and RCCS coupled. It was observed that PRCS alone is sufficient to reach the thermal comfort criteria with low energy consumption, but it was unable to maintain the background temperature. When PRCS coupled with RCCS, it gives maximum thermal comfort for high supply water temperature ranging from 17 °C to 21 °C.

Abstract

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

Abstract

This paper demonstrates the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies. In the first case, a building model is de-tuned by deliberately injecting faults into more than 60 parameters. This model was then calibrated using Autotune and its accuracy with respect to the original model was evaluated in terms of the industry-standard normalized mean bias error and coefficient of variation of root mean squared error metrics set forth in ASHRAE Guideline 14. In addition to whole-building energy consumption, outputs including lighting, plug load profiles, HVAC energy consumption, zone temperatures, and other variables were analyzed. In the second case, Autotune calibration is compared directly to experts’ manual calibration of an emulated-occupancy, full-size residential building with comparable calibration results in much less time. The paper concludes with a discussion of the key strengths and weaknesses of auto-calibration approaches.

Abstract

Selecting a high albedo (solar reflectance) waterproofing layer on the top of a roof helps lower the roof’s surface temperature and reduce the air conditioning energy consumption in the top floor of a building. The annual energy savings depend on factors including weather, internal loads, and building operation schedule. To demonstrate the energy saving potential of high albedo roofs, an apparatus consisting of two nearly identical test chambers (A and B) has been built in four Indian climates: Chennai (hot & humid), Bangalore (temperate), Jhagadia (Hot & dry) and Delhi (composite). Each chamber has well-insulated walls to mimic the core of an office building. Both chambers have the same construction, equipment, and operating schedule, differing only in roof surface. The reinforced cement concrete roof of Chamber A is surfaced with a low-albedo cement layer, while that of Chamber B is surfaced with a high-albedo water proof membrane (change in solar reflectance of 0.28). The experiment will be carried out for one year to explore seasonal variations in energy savings. Initial results in the month of July (post summer) shows that savings from high albedo roof ranges from 0.04 kWh/m2/day in temperate climates, to 0.08 kWh/m2/day in hot & dry climate.