Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

Jin, Wenquan; Ullah, Israr; Ahmad, Shabir; Kim, Do‐Hyeun

doi:10.3390/su11040997

Cited by 23 publications

(10 citation statements)

References 57 publications

(59 reference statements)

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“…The occupancy factor is an important explanatory variable in the indoor room temperature prediction since the inclusion of this variable gives an understanding of approximate heat generation due to the magnitude of occupancy in a building structure. Referring to Table I, previous studies (Gunay et al , 2014 and Jin et al , 2019) suggested that occupancy profile and thermal comfort are important and influential phenomenon for indoor room temperature forecasting. Set Point Temperature Control on a weekday/weekend gives a significant relationship with respect to the determination of indoor room temperature, as it controls the time duration of usage of HVAC controls with a defined set of threshold temperature range. This variable can be effectively used to achieve model predictive control in the feedback loop for energy optimization.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models

Pandey

Basu

2020

JM2

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Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications 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. Originality/value 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models

Pandey

Basu

2020

JM2

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“…In many applications, the goal of the predictive model is to serve as a component of an efficient smart home controller that automatically maintains the comfort levels of the inhabitants. Jin et al [21] uses a predictive model of temperature values to create a controller that maintains the comfort of the occupants of a home, while simultaneously optimizing the energy usage. The predictive model is based on an LSTM model trained on recorded values of indoor and outdoor temperature and humidity values.…”

Section: Temperature and Energy Management In Smart Homesmentioning

confidence: 99%

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

Burns

Fichthorn

Ling

et al. 2021

Sensors

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In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available.

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“…Above all, Jin et al [30] remarked that in order to improve user comfort, PMV is the most widely used model and was developed by Fanger in the 1970s through expensive laboratory experiments [31]. The PMV model is the basis of the International Organization for Standardization (ISO) 7730 standard, which was available in 1994 and 2005 versions [32,33].…”

Section: Overviewmentioning

confidence: 99%

Green BIM Assessment Applying for Energy Consumption and Comfort in the Traditional Public Market: A Case Study

et al. 2019

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This study focused on the energy consumption and environmental comfort of the traditional Xindian Central Public Retail Market. Established for more than 30 years, the market has been a crucial role in the daily life of local residents. Thus, the energy consumption and comfort level of the market are subjects of great concern. By using green building information modeling (BIM) simulation, which is an innovative assessment process that combines green buildings and BIM for the architecture, engineering, and construction (AEC) industries to achieve sustainability, this study explored the current situation of energy efficiency and comfort level of the market. A green BIM model of the market and surrounding area was constructed in Autodesk Revit. Subsequently, nine items pertaining to energy consumption and environmental comfort were selected from the green BIM model to conduct simulation by using the software package Integrated Environmental Solutions Virtual Environment (IES VE). Based on the IES VE simulation results, heat radiation was identified as one of the main causes of energy consumption in the market. Moreover, the results indicated problems of ventilation and insufficient sunlight inside the market. These analytical outcomes and optimization suggestions can be provided as references for retrofitting to obtain sustainable architectures in future.

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Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes

Cited by 23 publications

References 57 publications

Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models

Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

Green BIM Assessment Applying for Energy Consumption and Comfort in the Traditional Public Market: A Case Study

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