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The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.

A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.

ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.

The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.

Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.

This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.

A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.

Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a complex and challenging task, especially when energy demands are exponentially rising. The purpose of this paper is to review the relevant literature on indoor temperature forecasting in the past two decades and draw inferences on important methodologies with influencing variables and offer future directions.

The motivation for this work is based on the research work done in the field of intelligent buildings and energy related sector. The focus of this study is based on past literature on forecasting models and methodologies related to IRT forecasting for building energy management, with an emphasis on data-driven models (statistical and machine learning models). The methodology adopted here includes review of several journals, conference papers, reference books and PhD theses. Selected forecasting methodologies have been reviewed for indoor temperature forecasting contributing to building energy consumption. The models reviewed here have been earmarked for their benefits, limitations, location of study, accuracy along with the identification of influencing variables.

The findings are based on 62 studies where certain accuracy metrics and influencing explanatory variables have been reviewed. Linear models have been found to show explanatory relationships between the variables. Nonlinear models are found to have better accuracy than linear models. Moreover, IRT profiles can be modeled with enhanced accuracy and generalizability through hybrid models. Although deep learning models are found to have better performance for this study.

This is accuracy-based study of data-driven models. Their run-time performance and cost implications review and review of physical, thermal and simulation models is future scope.

Despite the earlier work conducted in this field, there is a lack of organized and comprehensive evaluation of peer reviewed forecasting methodologies. Indoor temperature depends on various influencing explanatory variables which poses a research challenge for researchers to develop suitable predictive model. This paper presents a critical review of selected forecasting methodologies and provides a list of important methodologies along with influencing variables, which can help future researchers in the field of building energy management sector. The forecasting methods presented here can help to determine appropriate heating, ventilation and air-conditioning systems for buildings.

In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices’ costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures.

A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated.

The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants’ comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day.

This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope.

This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits.

This study aims to develop an architectural prototype of a Cyber-Physical System (CPS), as well as lay a technological foundation for future smart housing with improved health and well-being outcomes for its occupants.

This study deploys smart sensors to monitor the key environmental parameters of a house. Using Internet of Things technology, a prototype of a CPS has been developed for capturing the environmental conditions over time. A case study involving a property in New Zealand was undertaken to validate the prototype.

The study proposes a monitoring platform, enabled by the CPS and smart sensing devices, that collects, shares, stores, analyses and visualises indoor environment data. The reliability and accuracy of the monitoring system were enhanced by comparing the activity of house occupants with sensor data.

Due to limited time, the prototype was tested in one house for a period of one month. Air quality was not considered in this study. However, the work suggests that such an approach provides an effective solution for government organisations and housing agencies to collect information for the purpose of assessing building thermal performance.

This research proposes a new lens consisting of a home environment monitoring application with health and well-being implications. It could also be used to inform the future design of healthy homes and buildings, both in New Zealand and internationally.

As the global population keeps increasing with its associated urbanisation and climate change issues being experienced in various degrees worldwide, there is the need to find mitigating measures to improve thermal conditions within spaces. The study aimed to evaluate green roofs to determine whether they could provide thermal comfort within residential buildings.

Forty-two-year weather data were retrieved from the Kumasi weather station to establish the pattern of the climatic variables. Furthermore, an experiment was conducted by constructing test cells to determine the potential of vegetation/green roofs on temperature development within spaces. This approach led to a simulation-based exploration of the thermal performance of the test cells to probe variables that could lead to the reduction in temperature after the models in the software (design-builder) had been validated.

The results on the 42 years (1976–2018) weather data showed a significant (p = 0.05) mean temperature increment of 2.0 °C. The constructed test cell with Setcreasea purpurea (Purple Heart) vegetation showed an annual mean temperature reduction of 0.4 °C (p = 0.05). In addition, the exploration using the simulation application showed combinations of various soil depth (70–500 mm) and leaf area indices (leaf area index of 2–5) having a potential to lower indoor temperature by 1.5 °C and its associated reduction in energy use. The option of green roofs as a valuable alternative to conventional roofs, given their potential in mitigating climate change, must be encouraged. A survey of occupants in six selected neighbourhoods in Kumasi showed varying subjective perceptions of several green issues (24–98%) and increases in temperature values because of the loss of greenery in the city.

Empirical data that point to the significant reduction of indoor temperature values and a subsequent reduction in energy use have been unearthed. Therefore, built environment professionals together with city authorities could invest in these sustainable measures to help humanity.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

A growing number of travelers seek well-being when traveling. As concerning about outdoor air pollution in tourism destinations escalates, little is known about indoor air pollution in hotel guestrooms. The purpose of the present study is to assess particulate matter (PM) pollution in US hotel guestrooms and to provide baseline indoor PM readings in occupied and unoccupied rooms.

A series of field tests and experiments monitoring PM levels were conducted in the guestrooms overnight – with and without occupants – using the sophisticated, industrial-grade PM-monitoring equipment.

The results revealed that PM levels were very low when rooms were unoccupied or when guests were asleep. However, unhealthy PM mass concentrations were observed in occupied rooms when guests engaged in physical activity such as showering and walking around or while room attendants cleaned rooms. Among the physical activities, room cleaning caused hazardous indoor PM pollution, reaching 1,665.9 µg/m³ of PM10 and 140.4 µg/m³ of PM2.5 although they tended to be brief.

Leveraging increasing guest demand in well-being is essential for sustainable business and further growth. Indoor air quality must be recognized as an important factor to be controlled for well-being and health of guests and employees. Major hotel brands should take it into consideration as they infuse well-being DNA into their products and culture.

To the best of the authors’ knowledge, this study is the first empirical investigation of PM pollution both in occupied and unoccupied hotel guestrooms in the USA, which reveals unhealthy PM pollution associated with the routine human activities in occupied guestrooms.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

Life experience in hot and arid areas of Iran has proved that in the transitional seasons (spring and autumn) in which the climate is not too hot, passive cooling systems such as windcatchers (baadgir) have functioned well. This paper intends to investigate the efficiency of a single-side windcatcher as a passive cooling strategy; the case study is the Bina House windcatcher, located in Khousf town, near Birjand city, Iran.

To achieve the aim, air temperature, relative humidity, wind data and mean radiant temperature were measured by the related tools over five days from September 23 to October 23. Then, the thermal performance of the windcatcher was examined by analyzing the effects of all these factors on human thermal comfort. Quantitative assessment of the indoor environment was estimated using DesignBuilder and its computational fluid dynamics (CFD) tool, a thermal comfort simulation method to compare the cooling potential of the windcatcher. Windcatcher performance was then compared with two other common cooling systems in the area: single-side window, and evaporative cooler.

The results showed that both windcatcher and evaporative cooler can provide thermal comfort for Khousf residents in the transitional seasons; but the difference is that an evaporative cooler needs to consume water and electricity power, while a windcatcher is a passive cooling system that uses clean energy of wind.

The present study, by quantitative study of single-side windcatchers in a desert region, measured the climatic factors of a historical house and compared it with thermal comfort criteria. Therefore, the results of field measurements were analyzed, and the efficiency of the windcatcher was compared with two other cooling systems, namely single-side ventilation and evaporative cooler, in the two seasons of summer and autumn (transition seasons).

The main objective of this paper is to obtain the duty‐cycle probability forecast functions of cooling and heating aggregated residential loads. The method consists of three steps: first, the single loads are modelled using systems of stochastic differential equations based on perturbed physical models; second, intensive numerical simulation of the stochastic system solutions is performed, allowing several parameters to vary randomly; and third, smoothing techniques based on kernel estimates are applied to the results to derive non‐parametric estimators, comparing several kernel functions. The use of these dynamical models also allows us to forecast the indoor temperature evolution under any performance conditions. Thus, the same smoothing techniques provide the indoor temperature probability forecast function for a load group. These techniques have been used with homogeneous and non‐homogeneous device groups. Its main application is focused on assessing Direct Load Control programs, by means of comparing natural and forced duty‐cycles of aggregated appliances, as well as knowing the modifications in customer comfort levels, which can be directly deduced from the probability profiles. Finally, simulation results which illustrate the model suitability for demand side – bidding – aggregators in new deregulated markets are presented.