Abstract

There is an emerging consensus that the future of intelligent and energy efficient homes is automation and cloud control. This paper presents a novel infrastructural concept using smart ceiling tiles and an energy-harvesting Bluetooth Low Energy (BLE) switch to control home appliances. The switch location is determined using trilateration of the signals received by the tiles installed in different rooms. In this paper, we localized the switch among three rooms separated by a brick wall by analyzing the received signal strength indicator (RSSI) values captured by three tiles located in three rooms. The tiles are powered with DC power using the support rails, thus minimizing wiring for power delivery. We used different machine learning classification algorithms with the received RSSI values as features for predicting the location of the switch accurately. Through experimentation, we achieved high accuracy of 93% in classifying the switch position among the rooms. The integration of smart tiles in the false ceiling along with the use of an energy-harvesting BLE switch offers numerous advantages, such as reducing wiring complexity, enhancing user convenience, and aesthetic design.

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the conventional system, but the main drawback of radiant technology is inability to cater the latent load and condensation. This problem has engaged researchers to push their emphasis on Personalized Conditioning System (PCS). It forms a micro-climate region near the occupant. This study is aimed at PCS combined with a radiant conditioning system, which evaluates the performance of the PCS and compares it with different radiant cooling systems. A radiant cubicle was used to investigate the performance of the PCS for both heating & cooling modes. For heating mode, the Standard Effective Temperature (SET) was calculated with the help of thermal comfort surveys which were in a range of 23.4°C to 27.3°C. For cooling mode, four different cases compared the performance of the system which are: Conventional Air …

Abstract

Residential energy feedback refers to is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback studies based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental

Abstract

The demand for energy in residential structures is set to outstrip the supply soon. Most studies rely on national databases or previously published datasets. It is crucial to approach this topic using a bottom-up methodology. Therefore, this research aims to analyse the energy consumption pattern of residential buildings in Jaipur by utilizing primary datasets. The present study takes Jaipur as a case study and proposes a way to define architectural archetypes in Indian towns based on their energy efficiency. The collection and analysis of 2,327 primary data samples included energy use statistics and socioeconomic information. This research applies multivariate analysis in a bottom-up manner to a primary database. The clustering method was utilized to determine the energy consumption of Indian families in Jaipur. Low- and middle-income families tend to purchase more appliances when their income increases, yet …

Abstract

Global access to electricity has increased from 78.2% to 2000 to 90.5% in 2020, resulting in an increased electricity demand worldwide. Unlike commercial electricity consumption, which is managed by professionals, residential consumption is managed by the householders, who often lack insight into their energy usage. Quality feedback, including detailed energy consumption and tips, can lead to substantial household savings. There are several mediums for providing energy feedback, such as Short Message Service (SMS), postal letter, email, mobile app, and In-Home Display (IHD). Studies suggest that feedback through electronic media can save up to 20% of energy consumption. In this work, we aim to design mobile application interfaces that can maximize energy savings through efective feedback. The level of savings realized is dependent on the user’s preferences and understanding of the information presented. User preferences are subjective of their profle (e.g., age, occupation, income) and the cultural context (e.g., country). The possibility of energy reduction is high when the provided information matches the user preferred information for feedback. Smart homes have recently been included as an annexure in India’s building energy code (Eco Niwas Samhita 2021), indicating a growing demand for quality energy feedback in India. However, there is a lack of research that addresses what feedback information is suitable for Indian users. We conducted two questionnaire-based surveys, one to understand users’ preferences for feedback information and another to validate the designed mobile application interface screens. The surveys were conducted on two age groups, young and middle-aged adults. A Chi-Square Test of Independence was performed to assess the relationship between the user’s preference for feedback information and their age group. Participants identifed total energy consumption, appliance level disaggregated information, energy-saving tips, goals, and historical consumption comparisons as the top fve information types. In contrast, normative comparison was the least preferred information. The follow-up design validations suggest that the interface should be customizable to accommodate the varying preferences of users. The current fndings will help customize the energy feedback display UI design as per the Indian population. Keywords: Energy-feedback, Residential, Interface-design, User centric feedback, In-home-display

Abstract

In developing economies, residential energy consumption patterns have rapidly transformed with better energy access and service quality. Unlike other building types, residential buildings are more complex due to wide variations in their consumption patterns influenced by various factors. Researchers have characterised residential building stock based on distinct building archetypes. This paper presents a comprehensive review of relevant published research focusing on the classification of residential buildings based on their energy consumption. This review also focuses on residential archetype studies in the context of building science. The methodologies adopted by different researchers to characterise the energy use of residential building stock using an archetypal approach at different spatial scales (building to city scale and local to national scale) have been critically reviewed in this study. The paper will provide the researchers with a holistic understanding of the current directions and magnitude of ongoing research in this domain. KEYWORDS: Housing stockbuilding archetypesclassification methodologybuilding energy consumptionenergy modellingbottom-up studies Previous article View issue table of contents Next article Disclosure statement No potential conflict of interest was reported by the author(s). Additional information Funding This work is supported by a Joint Indo-UK research project called ‘Residential building energy demand reduction in India (RESIDE),’ funded by the Department of Science and Technology (DST/TMD/UK-BEE/2017/07), Government of India, and the Engineering and Physical Sciences Research Council (EP/R008434/1), United Kingdom. Related research People also read Recommended articles Cited by Identifying building archetypes based on energy performance as the major criteria: a case of Jaipur, India Bibhu Kalyan Nayak et al. Advances in Building Energy Research Published online: 27 May 2023 Data-driven approach to prioritize residential buildings’ retrofits in cold climates using smart thermostat data Aya Doma et al. Architectural Science Review Published online: 31 Mar 2023 Review of studies on thermal comfort in Indian residential buildings Sunil Kumar Sansaniwal et al. Science and Technology for the Built Environment Published online: 27 Feb 2020 View more

Abstract

Climate classification simplifies the representation of expected climate conditions by eliminating the need to describe each climatic parameter. Climate classification is frequently used as a basis for recommendations through codes and standards for buildings because a given climate has nearly similar design conditions. Furthermore, building energy efficiency programmes are designed based on the climatic classification to determine appropriate design strategies to eliminate the under-design or over-design of buildings for similar climatic regions. This necessitates the use of realistic and reliable climate classifications. Over the past two decades, numerous research studies have proposed climate classification methods. However, there is a lack of universal acceptance of climate classification methodology to balance complexity and accuracy. This research provides an overview of various climate classification …

Abstract

Climate classification plays a significant role in the development of building codes and standards. It guides the design of buildings’ envelope and systems by considering their location’s climate conditions. Various methods, such as ASHRAE Standard 169, Köppen, Trewartha utilize climate parameters such as temperature, humidity, solar radiation, precipitation, etc., to classify climates. When establishing requirements in building codes and standards, it is crucial to validate the classification based on the building’s thermal loads. This paper introduces a novel methodology for classifying cities based on the number of similar days between them. It calculates similarity using daily mean temperature, relative humidity, and solar radiation by applying threshold values. A matrix of similar days is analyzed through agglomerative hierarchical clustering with different thresholds. A scoring system based on building thermal load, where lower scores signify better classification, is employed to select the best method. The method was tested using U.S. weather data, yielding a lower score of 54.5 compared to ASHRAE Standard 169’s score of 63.09. This suggests that the new approach results in less

Abstract

In recent years, semi-transparent building-integrated photovoltaics (BIPV) technology has attracted attention for its renewable energy utilization. This research aims to develop a methodology for assessing the energy-saving potential of semi-transparent photovoltaic (STPV) window, which has a complex relationship with daylighting and air conditioning (AC) electricity consumption. A case study has been developed for the composite climate of Hyderabad, India. The study examines four commercially available single and double pane STPV windows and similar non-photovoltaic windows. For evaluation, an automated parametric simulation tool was developed to compute the net electricity consumption (NEC) for a representative model building with different window systems, and several window-to-wall ratios (WWR) and orientations. The result shows benefit in adopting the STPV window system across all directions and higher optimal WWR in STPV window as compared to normal window with the same opto-thermal characteristics.

Abstract

When the Indian government declared the frst lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of fve buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To ofset the diference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown.