Evaluating energy consumption, indoor air quality, and student productivity

Smart energy conservation research is gaining traction in a variety of industries throughout the world. The current research is projected to cut energy consumption in the construction sector, which has already reached 49% globally and is expected to rise by 2% annually, costing millions of dollars per month. Balancing energy savings with thermal satisfaction is a current difficulty, as most researchers have concentrated on attaining energy savings without reaching the thermal contentment of the occupant, which could pose a health risk to both young and old occupants. To address the problem, we conducted empirical studies with 193 participants in the Northern part of Nigeria, where they were exposed to an indoor temperature of 22°C to determine their thermal environment, choice and comfort votes, viewed and favored control, and overall thermal satisfaction, which will help calculate and define the unused thermal satisfaction thermostat and later.

Research on the Indoor Thermal Environment of Attached Sunspace Passive Solar Heating System Based on Zero-State Response Control Strategy

The application of attached sunspace passive solar heating systems (ASPSHS) for farmhouses can improve building performance, reduce heating energy consumption and carbon dioxide emissions. In order to take better use of the attached sunspace to prevent heat transfer or promote natural ventilation, this paper presented a zero-state response control strategy for the opening and closing time of active interior window in the ASPSHS. In order to verify the application of this strategy, an attached sunspace was built in an actual farmhouse. A natural ventilation heat exchange model was built based on the farmhouse with attached sunspace. The proposed zero-state response control strategy was implemented in TRNSYS software. Field measurement in living lab was carried out to inspect the distribution of the thermal environment in the farmhouse with attached sunspace under a zero-state response control strategy in the cold region of northern China. The experimental results show that, even under −5.0–2.5 °C ambient temperature, the application of zero-state response control strategy effectively increases the internal temperature to an average of 25.45 °C higher than the outside, with 23% indoor discernible temperature differential in the sample daytime. The whole-season heating performance was evaluated by simulating the model for the heating season in 2020–2021. The simulation demonstrates that the ASPSHS under zero-state response control strategy can maintain a basic indoor temperature of 14 °C for 1094 h during the heating season, with a daytime heating guarantee rate of 73.33%, thus ensuring higher indoor heating comfort during the day. When compared to a farmhouse with an attached sunspace under the zero-state response control strategy, the energy savings rate can be enhanced by 20.88%, and carbon emissions can be reduced by 51.73%. Overall, the attached sunspace with the zero-state response control strategy can effectively increase the indoor temperature when the solar radiation is intensive and create a suitable thermal environment for the farmhouse in the cold region of northern China.

Design of Monitoring and Controlling System for Smart Home

An intelligent lifestyle has become a hotspot for researchers and industries nowadays. The smart home monitoring and controlling system with the Arduino as the main controller is designed in this paper, combined with sensors, Wi-Fi, and cloud technologies. Various sensors collect household environmental information, such as indoor temperature and humidity, soil moisture, combustible gas concentration, and light intensity. The main controller processes the collected signals and automatically operates the devices, including a refrigeration equipment, water pump, buzzer, fan, stepping motor. The data can also be transmitted to the cloud platform through Wi-Fi for processing, and the home environment information and device can be remotely monitored and controlled by the cloud platform or smartphone APP.

A SIMULATION BASED STUDY OF ENERGY CONSERVATION OF RESIDENCES IN LAHORE, PAKISTAN

A house in composite climate of Lahore (Pakistan) needs intensive cooling in summers; energy recovery ventilation to reduce humidity during monsoon and comfortable indoor temperature during winters. All these conditions have to be fulfilled with a reduced load on energy resources. Recent trends in construction and design of residential buildings in Pakistan symbolize uncontrolled use of energy resources. There is no data available with planning and developing authorities of housing sector that shows an account of energy loads of built houses. The potential of conservation of energy will be analyzed by actually studying the cooling and heating loads of recently constructed houses Key Words: Energy Conservation Potential, Module Study, Simulations

Evaluación térmica y lumínica en prototipos de cubiertas ligeras, para clima cálido húmedo

The zinc sheet roof is one of the most popular elements in Latin American architecture, and in many other regions with warm humid climates. Creating lighting and thermal alternatives focused on this typology would imply major benefits in the environmental and social fields. This study carried out in Manabí, Ecuador, evaluates three prototypes of light roofs, combining zinc with PVC, in order to determine the correct configuration of translucent material to create environments that are within thermal and lighting parameters. The results indicate that the empirical solutions model has the lowest variation in indoor temperature, with 32.63%, unlike the 32.97% of the cross-type model, and the 34.40% of the side strip model. Additionally, it was seen that the greatest influence of solar radiation on the roof is recorded from 1:00 p.m. to 2 p.m. approximately

Estimating Cardiovascular Health Gains From Eradicating Indoor Cold in Australia

Background Exposure to cold indoor temperature (<18 degrees Celsius) increases cardiovascular disease (CVD) risk and has been identified by the WHO as a source of unhealthy housing. While warming homes has the potential to reduce CVD risk, the reduction in disease burden is not known. We simulated the population health gains from reduced CVD burden if all homes in Australia were adequately warm. Methods The health effect of eradicating cold housing through reductions in CVD was simulated using proportional multistate lifetable model. The model sourced CVD burden and epidemiological data from Australian and Global Burden of Disease studies. The prevalence of cold housing in Australia was estimated from the Australian Housing Conditions Survey. The effect of cold indoor temperature on blood pressure (and in turn stroke and coronary heart disease) was estimated from published research. Results Eradication of exposure to indoor cold could achieve a gain of undiscounted one and a half weeks of additional health life per person alive in 2016 (baseyear) in cold housing through CVD alone. This equates to 0.447 (uncertainty interval: 0.064, 1.34; 3% discount rate) HALYs per 1,000 persons over remainder of their lives through CVD reduction. One-fifth of the total health gains are achievable between 2016 and 2035. Although seemingly modest, the gains outperform currently recommended CVD interventions including dietary advice for adults (0.017 per 1000 people, UI: 0.01, 0.027), lifestyle program for adults (0.024, UI: 0.01, 0.027) and Community Heart Health Program (0.141, UI: 0.071, 0.221). Conclusion Cardiovascular health gains achievable through eradication of cold housing are comparable with lifestyle and dietary interventions. The benefits of housing improvement are also substantial in other social domains (comfort, heating bills and energy efficiency).

Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques

This paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of thermal comfort for different pairs of indoor temperature and humidity values according to activity, time, and season, verified in the workplace, were obtained. The results obtained were compared to other statistical models of linear regression used for thermal comfort, thus observing that comfort temperature values are within a same range, yet the network offered more information since a range of options for interior design parameters (temperature/relative humidity) was offered for different work, time, and season conditions. Additionally, if compared with static models of heat exchange, the contribution of Bayesian networks is noted when considering a context of actual operability and adaptability conditions to the environment, which is promising for developing thermal comfort intelligent systems, especially for the development of sustainable settings within the Industry 4.0 paradigm.

Indoor temperature, humidity and illumination control system based on Android

The temperature, humidity and illumination control system designed in this paper is based on Android platform. It is controlled by connecting the mobile phone wifi with the single chip microcomputer, and uploaded to the mobile phone client through wifi serial port technology. Users can collect indoor temperature and humidity data and illumination data through sensors. Through the mobile phone client to control the LED lights and relay switches of household appliances, such as the control of air conditioning, and finally achieve the goal of adjusting indoor lighting and controlling the on-off of household appliances.

Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.