Search Results (38)

In the context of the architecture and construction industries implementing environmentally friendly sustainable solutions, smart glass is also designed using sustainable materials and sustainable operations, contributing to environmental protection efforts. The application of smart glass in large buildings optimizes the buildings’ operation, helping to reduce reliance on air conditioners that consume large amounts of electricity and emit significant amounts of CO₂ into the environment. Additionally, smart glass enhances energy-saving efficiency by intelligently regulating the indoor temperature and natural lighting, thereby reducing overall energy consumption and operational costs. In this study, the authors propose a fuzzy multi-criteria decision-making (MCDM) model to evaluate and select optimal sustainable smart glass technology based on technical, environmental, economic, and social criteria. The model integrates the fuzzy analytic hierarchy process (FAHP) to calculate the weights of the criteria and the Interactive and Multi-criteria Decision-Making in Portuguese Model (TODIM) to rank different smart glass technology alternatives. The findings highlight the most suitable smart glass technology, offering significant benefits in reducing energy consumption while ensuring aesthetic appeal and durability. This study provides a comprehensive approach to integrating sustainability into smart glass design, contributing to the development of green and energy-efficient buildings. Full article

This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communication without additional wiring. The system is tested under two conditions: participating in ADRPs via OpenADR and not participating in ADRPs. An 8.8% load reduction is observed with different temperature settings when not participating in ADRPs, and energy reductions of 21% to 46% are achieved using various cooling/fanning duty cycles in ADRPs. The proposed system can be integrated with any DR algorithm to meet demand management requirements under the OpenADR program, contributing to significant demand reductions. Full article

This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart meters within a Home Energy Management System (HEMS). Additionally, a meter was installed on the distribution board to measure total consumption. Real-time data were collected at 15-min intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre, inhabited by four people. This setup enabled the capture of detailed and specific energy consumption data, facilitating data analysis and validating the system before large-scale implementation. Using the detailed power consumption information of these devices, an LSTM model was trained to identify temporal connections in power usage. Proper data preparation, including normalisation and feature selection, was essential for the success of the model. The results showed that the LSTM model was effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This study emphasises the importance of continued research on preferred predictive models and identifies areas for future research, such as the integration of additional contextual data and the development of practical applications for residential energy management. Additionally, it demonstrates the potential of LSTM models in smart-home energy management and serves as a solid foundation for future research in this field. Full article

This study identified the practices of adaptation strategies to climate change in Jimma, Bedelle, Bonga, and Sokorru urban centers using a survey of 384 households, 55 key informant interviews, 4 focus group discussions, and field observations. A cross-sectional study design was employed from 2019 to 2021. The adaptive capacity of municipalities to reduce climate extreme events was rated as poor by the majority (51%), mostly reactive measures (76%). The climate hazards identified in four urban centers were riverine and flash floods, urban heat waves, landslides, and windstorms. The urban households practiced lifestyle modification, reduce paved surfaces, the use of air conditioner, planting trees, and multiple windows. The adaptation strategies practiced by municipalities include the relocation of prone areas, the support of basic amenities, the construction of protection walls, diversion ditches, the clearance of waterways and rivers, greenery, and park development. The adaptation actions were constrained by a lack of awareness, commitment, cooperation and coordination, adaptive capacity, and participation. Gray/physical infrastructures (costly but important) as adaptation actions were hampered by the low municipal capacity. We recommend that urban authorities should incorporate climate change adaptation strategies into urban planning and development proactively to ensure future resilient climate smart urban centers of southwest Ethiopia. Full article

This work proposes a data-driven decision-making approach to develop a smart avatar that allows for evaluating the thermal comfort experienced by a user in Chile. The ANSI/ASHRAE 55-2020 standard is the basis for the predicted mean vote (PMV) comfort index, which is calculated by a random forest (RF) regressor using temperature, humidity, airspeed, metabolic rate, and clothing as inputs. To generate data from four cities with different climates, a 3.0 m × 3.0 m × 2.4 m shoe box with two adiabatic walls was modeled in Rhino and evaluated using Grasshopper’s ClimateStudio plugin based on Energy Plus+. Long short-term memory (LSTM) was used to forecast the PMV for the next hour and inform decisions. A rule-based decision-making algorithm was implemented to emulate user behavior, which included turning the air conditioner (AC) or heater ON/OFF, recommendations such as dressing/undressing, opening/closing the window, and doing nothing in the case of neutral thermal comfort. The RF regressor achieved a root mean square error (RMSE) of 0.54 and a mean absolute error (MAE) of 0.28, while the LSTM had an RMSE of 0.051 and an MAE of 0.025. The proposed system was successful in saving energy in Calama (31.2%), Valparaiso (69.2%), and the southern cities of Puerto Montt and Punta Arena (23.6%), despite the increased energy consumption needed to maintain thermal comfort. Full article

AI is playing an important role in promoting sustainable development, but the carbon footprint caused by AI is scaling quickly and may partly offset the effort to reduce carbon emissions. However, recommendations for limiting the AI carbon footprint are lacking. In order to address this gap in the literature, this paper first constructs a tripartite evolutionary game model by taking governments, AI industry alliances, and consumers into consideration, and then exploring the impacts of key factors on these three players’ strategy selection based on the case of smart air conditioner consumption in China. The results show that the behavior of governments has an important influence on the behavior of AI industry alliances and consumers. The ideal consequence is that governments adopt an unregulated strategy, AI industry alliances adopt a green development strategy, and consumers adopt a green purchase strategy. Regulation by governments is indispensable for limiting the AI carbon footprint during an early stage but becomes dispensable when the system reaches an optimal state. Although a tendency toward green consumption, image benefit, regulatory cost, carbon price, and the subsidies given to consumers and AI industry alliances can largely influence the strategy selection of governments, governments are most sensitive to carbon prices and the subsidies given to consumers. AI industry alliances are not sensitive to subsidies, reputation improvement, and reputation loss but are most sensitive to carbon prices. Consumers are most sensitive to green consumption tendencies, self-satisfaction, and utility but are not sensitive to subsidies. Full article

With the growing prevalence of modern technologies as part of everyday life, mid-air gestures have become a promising input method in the field of human–computer interaction. This paper analyses the gestures of actual users to define a preliminary gesture vocabulary for home air conditioning (AC) systems and suggests a gesture vocabulary for controlling the AC that applies to both home and vehicle environments. In this study, a user elicitation experiment was conducted. A total of 36 participants were filmed while employing their preferred hand gestures to manipulate a home air conditioning system. Comparisons were drawn between our proposed gesture vocabulary (HomeG) and a previously proposed gesture vocabulary which was designed to identify the preferred hand gestures for in-vehicle air conditioners. The findings indicate that HomeG successfully identifies and describes the employed gestures in detail. To gain a gesture taxonomy that is suitable for manipulating the AC at home and in a vehicle, some modifications were applied to HomeG based on suggestions from other studies. The modified gesture vocabulary (CrossG) can identify the gestures of our study, although CrossG has a less detailed gesture pattern. Our results will help designers to understand user preferences and behaviour prior to designing and implementing a gesture-based user interface. Full article

In recent years, the increasing winter load peak has brought great pressure on the operation of power grids. The demand response on the load side helps to alleviate the expansion of the power grid and promote the consumption of renewable energy. However, the response of large-scale electric heat loads to the same electricity price curve will lead to new load peaks and regulation failure. This paper proposes a grouping coordinated preheating framework based on a demand response model, which realizes the interaction of information between the central controller and each regulation group. The room thermal parameter model and the performance map of the inverter air conditioner/heat pump are integrated into the demand response model. In this framework, the coordination mechanism is adopted to avoid regulation failure, an edge computing structure is applied to consider the users’ preferences and plans, the grouping and parallel computing structure is proposed to improve the computing efficiency. Users optimize their heat load curves based on a demand response model, which can consider travel planning and ensure user comfort. The central controller updates the marginal cost curve based on the predicted scenario set to coordinate the regulation groups and suppress the new peaks. The simulation results show that the proposed method can promote the consumption of renewable energy through coordinated preheating and reduce the system energy consumption cost and user bills. The parallel computing structure within the regulation group also ensures the computing efficiency under large-scale loads. Full article

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case. Full article

Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses. Full article

In response to the future net zero emissions plan in Taiwan, the campus shoulders the university’s social responsibility and educational significance. Recently, energy conservation and carbon reduction have become common goals for every campus. However, there is a common problem to be addressed in terms of how to take into account the economic benefits and the continuous improvement strategy regarding actual demands. In this study, a systematic design of energy-saving action plans for a Taiwan campus by considering economic benefits and actual demands is demonstrated. By taking National Taiwan University of Science and Technology in Taiwan as an example, eight energy-saving action plans during the period from 2016 to 2020 are introduced, and the effectiveness of these topologies is verified by real implementations. Action plans contain the installation of a smart energy management system, elevator power recovery devices, circulating fans, and lighting delay switches; the replacement of old air-conditioners, fluorescent lamps, and high-sodium streetlights; and load consolidation and low-efficiency transformers replacement. Upon evaluation, the estimated electricity savings can achieve up to 16% of annual electricity consumption, and the payback period is about 5.22 years. Upon application, the actual power saving amounts to about 3,076,260 kWh for approximately 15.5% of the power consumption of Taiwan Tech in 2020. Full article

In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system. Full article

This paper investigates the response and characteristics of the narrowband power line communication (NB-PLC) technique for the effective control of electric appliances such as smart air conditioners (SACs) for demand side management (DSM) services. The expression for temperature sensitivity by examining the influence of atmospheric temperature variations on power consumption profile of all possible types of loads, i.e., residential, commercial, and industrial loads is derived and analyzed. Comprehensive field measurements on these power consumers are carried out in Lahore, Pakistan. The responses of low voltage channels, medium voltage channels, and transformer bridge for a 3–500 kHz NB-PLC frequency range are presented for DSM services. The master control room transmits control commands for the thermostat settings of SACs over power lines, crossing the transformer bridge to reach the SACs of power consumers by using communication protocol smart energy profile 1.0. The comparison of hourly and daily power consumption profiles under evaluation loads, by analyzing typical and variable frequency air conditioners on setting thermostat temperature at 25 °C and 27 °C conventionally and then by using DSM control technique, is analyzed. A prominent reduction in power consumption is found with the implementation of the DSM control technique. Full article

Microgrid (MG) is a novel concept for a future distribution power system that enables renewable energy sources (RES). The intermittent RES, such as wind turbines and photovoltaic generators, can be connected to the MG via a power electronics inverter. However, the inverter interfaced RESs reduce the total inertia and damping properties of the traditional MG. Consequently, the system exhibits steeper frequency nadir and the rate of change of frequency (RoCoF), which may degrade the dynamic performance and cause the severe frequency fluctuation of the system. Smart loads such as inverter air conditioners (IACs) tend to be used for ancillary services in power systems. The power consumption of IACs can be regulated to suppress frequency fluctuation. Nevertheless, these IACs, regulating power, can cause the deviation of indoor temperature from the temperature setting. The variation in indoor temperature should be controlled to fulfill residential comfort. This paper proposes a multi-objective decentralized model predictive control (DMPC) for controlling the power consumption of IACs to reduce MG frequency fluctuation and control the variation in indoor temperature. Simulation results on the studied microgrid with the high penetration of wind and photovoltaic generator demonstrate that the proposed DMPC is able to regulate frequency deviation and control indoor temperature deviation as a user preference. In addition, the DMPC has a superior performance effect to the proportional-integral (PI) controller in terms of reducing frequency deviation, satisfying indoor temperature preferences, and being robust to the varying numbers of IACs. Full article

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices. Full article