Abstract

Residential electricity consumption datasets are essential for
applications such as smart grid management, home automation, renewable
energy integration, infrastructure planning, and policy-making. However,
obtaining high-resolution residential datasets remains challenging
due to the high costs and complexities of sensor installation, monitoring,
and maintenance, obtaining approvals and related human factors, among
other issues. To address this issue with a focus on the Indian residential
context, where such data is quite limited, we propose a Residential
Electricity Usage Simulator (REUS), to generate synthetic residential
electricity usage data. Our approach models electricity usage for 7 different
categories of homes using data collected over one year at an hourly
interval, from 65 residences. In addition to energy data, we also collected
18 different features for each home to improve our modeling. The
data and features are preprocessed using feature selection with Probabilistic
Finite State Machines (validated through Multiple Correspondence
Analysis) and further refined through systematic data cleaning
and imputation. We built simulation models using the popular Machine
learning techniques such as Long Short-Term Memory networks, including
Vanilla, Stacked, BiDirectional, and Encoder-Decoder LSTMs and
Transformer model, including Vanilla Transformer and Temporal Fusion
Transformer. In addition, statistical techniques such as Markov Chains
of orders (0, 1, 2, 3) and ARIMA were used as benchmarks to evaluate
the models' ability to generate a synthetic residential electricity dataset
that is close to real data. Via extensive experimentation and analysis,
our results show that (Bi-di) LSTMs capture the trends in electricity
consumption more effectively (with the lowest RMSE) than the other
models. Simulation and analysis of this nature enables broader, regionspecific
energy research, reducing the need for costly or intrusive data
collection.

Abstract

Natural ventilation (NV) is vital for optimizing energy efficiency, particularly when outdoor conditions are favourable. In highly polluted regions like National Capital Regions of Delhi (Delhi-NCR), occupants often avoid NV during pollution episodes, relying instead on mechanical systems. This behaviour stems from the assumption that outdoor air is consistently unsuitable for ventilation, even when pollution levels are within acceptable limits. As a result, energy consumption increases, even during periods when outdoor air could support NV.

This study integrates PM2.5 and PM10 data to develop a pollution-aware NV strategy for residential buildings. Behavioural insights gathered from detailed ethnographic interviews in Delhi-NCR highlight occupant tendencies to keep windows closed during pollution episodes, often based on perceived air quality rather than actual data. While previous studies have examined the effects of pollution on NV, few have directly linked measured air quality data with real occupant behaviour in residential settings. The proposed framework addresses these behavioural patterns. If outdoor PM2.5 and PM10 levels exceed standard thresholds, occupants are advised to close windows to maintain indoor air quality. Alternatively, when these levels fall below predefined thresholds, occupants are encouraged to open windows, highlighting the potential for energy savings. For the above-mentioned scenarios building energy simulation was employed to simulate NV strategies in a mixed mode building. This pollution-aware approach bridges the gap between occupant behaviour and actual environmental conditions, promoting energy-efficient ventilation practices in regions affected by air pollution. Our results demonstrate that aligning behavioural NV strategies with actual data addresses air quality concerns, contributing to energy-efficient and occupant-centric ventilation solutions.

Abstract

Smart home technology integrates various devices and systems that upgrade the convenience, security, and efficiency of a home. Due to these benefits, this technology is gaining global attention. In this paper, we have explored the perspectives of the residents of Mumbai on smart home technology adoption. A survey of 425 respondents was conducted, over a period of 3 months, covering demographics, awareness, preferences, concerns, and willingness to adopt. The key factors influencing their willingness to adopt such technology have been identified. The findings reveal insights into the perceived benefits, barriers, and demographic influences on smart home technology adoption in Mumbai. Most respondents show a high level of willing-ness to install smart home technology. More than 40% of respondents con-sider security their top priority when thinking about smart home features. Data privacy and security are the top concerns among the respondents, as they are worried about their personal data management and device vulnerabilities. The results are valuable for policymakers, advertisers, and developers working to promote smart home technology in the urban areas of India.

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

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

Residential District Cooling Systems (DCS) are crucial for maintaining thermal comfort in urban areas, making it imperative to understand occupant cooling behavior as it significantly influences DCS operation. While several studies have investigated cooling behavior within small user groups through on-site measurements or surveys, these often fall short in representing the broader population. In this study we considered 387 homes in Hyderabad, which has a DCS connection for chilled water supply. Operational data spanning three months across different seasons were meticulously selected to assess the residents' behavior for the cooling demand. In order to better facilitate the operation of the residential DCS, user load profile and system performance were analyzed using real world data for different seasons in the year. Our findings revealed notable disparities in DCS electrical consumption, with summer usage being 1.9 times higher than the monsoon period and 2.7 times higher than the winter period. Furthermore, a strong positive correlation emerged between outdoor temperature and thermal energy usage by the residents. On average, the daily thermal consumption per residence during winter, summer and monsoon is 3.3 kWhth, 35.1 kWhth and 10.4 kWhth respectively. Interestingly, the probability of a residence using AC during the day for the winter, summer and monsoon seasons are 0.07, 0.41 and 0.18 respectively.

Abstract

This study used IoT-based smart plugs and energy meters to collect device-level electricity data from homes for understanding the seasonal load patterns. The electrical energy consumption of five air conditioners (ACs) and five geysers was monitored using smart plugs during summer and winter to understand the power demand for cooling and hot water consumption. The study offered insights into the seasonal power usage patterns of these appliances. Further, a detailed analysis of daily electrical energy consumption in two homes was conducted to understand disaggregated electricity consumption. Five common household appliances (refrigerator, washing machine, television, water purifier, and microwave oven) were monitored using smart plugs in these homes. Along with smart plugs, smart energy meters were installed in these homes to monitor the total household electricity consumption. Their daily electricity consumption in summer was twice that of winter.

Abstract

This study aims to understand residential electricity consumption patterns using appliance load profiles of significant appliances in five residential units. To create load profiles, empirical electricity data from households and appliances was collected every second between May 1, 2023, and June 30, 2023, across five households in Hyderabad, India using device-level monitoring. A total of 48 appliances representing 12 distinct categories were monitored, and load profiles were created for four major appliances, namely air conditioner, geyser, refrigerator and washing machine for peak summer. It was found that these four appliances contribute to approximately 65% of the daily energy consumption. Air conditioners (AC) consumed most of the total daily energy and were primarily operated during solar off-peak hours, while geysers and washing machines were used close to solar peak hours. Hence, shifting geyser and washing machine usage can redirect 1.21 kWh per home in daily energy to solar peak hours. Moreover, improving the efficiency of AC by 10% and using heat pumps in place of geysers could lead to a 0.79 kWh reduction in daily energy consumption. Additionally, including thermal storage can entirely shift the AC energy usage to the daytime.

Abstract

Load diversity plays a pivotal role in accurately estimating cooling loads across various regions, which is essential for assessing the feasibility and impact of a District Cooling System (DCS). This study focuses on a typical residential DCS, serving a five-towered apartment complex with 387 residences, providing chilled water for space cooling. Our research delves into the analyze of residential air conditioning (AC) usage within the DCS during peak summer conditions in Hyderabad, India, in May 2022. By examining cooling data of residences and electricity data of DCS alongside system details, this study delineates the load profile and average daily AC consumption for May amounting to 34.3 kWhth (9.7 TRh) per residence. During peak usage, the thermal loading on the chiller reaches 75.8% of its total capacity, and while an average day, it varies between 21% to 61% of the chiller capacity.

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 …