Building Energy Use Prediction Owing to Climate Change

Im, Haekyung; Srinivasan, Ravi; Fathi, Soheil

doi:10.1145/3363459.3363531

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…Fathi and Srinivasan (2019) used temperature, solar radiation, and humidity as meteorological factors [20]. Daut et al ( 2012) also revealed a strong linear relationship between solar radiation and surface temperature [26]. Therefore, solar radiation was added to the four climate variables as a final list in this study.…”

Section: Meteorological Variablesmentioning

confidence: 98%

“…It should be noted that Kikumoto et al did not include the building characteristics in predicting heat loads [13]. Whereas Im et al concentrated on interpreting the regression models to explain the relationships among building characteristics, energy consumption, and weather [26,27], Im et al (2019) developed polynomial regression models on chilled water (CHW) and ELC consumption of campus buildings by comparing BEMs with hourly and daily data [24]. Additionally, Im et al (2020) attempted to predict CHW with lasso regression models using future weather data [25].…”

Section: Variable Selectionmentioning

confidence: 99%

“…The preliminary studies by Im et al used two independent meteorological variables: (1) outdoor air temperature and (2) relative humidity, representing years 2015, 2016, and 2054 [26,27]. Common factors were selected among 15 historical meteorological variables and 11 future meteorological variables.…”

Section: Meteorological Variablesmentioning

confidence: 99%

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The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics

Srinivasan

Maxwell

et al. 2022

Buildings

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Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios.

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Section: Meteorological Variablesmentioning

confidence: 98%

Section: Variable Selectionmentioning

confidence: 99%

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The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics

Srinivasan

Maxwell

et al. 2022

Buildings

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“…A case study of three campus buildings in Tianjin predicted that the average electricity consumption of one occupant varies depending on the function of the building and the mode of control of electrical equipment [28]. Various developed statistical regression models are used to understand the relationship between each variable and energy consumption [29]. In another earlier research study, it was revealed that enhanced modeling could be used in other types of buildings as long as it had an energy consumption monitoring platform, not limited to campus buildings [30].…”

Section: Introductionmentioning

confidence: 99%

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Desmira

Bakar

Wiryadinata

et al. 2022

IJEECS

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The energy use that is in <span lang="EN-US">excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neuro-fuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration.</span>

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“…Such data can be used for efficient decision making at various levels. There is a constant need for enhancing infrastructure performance through leveraging the digital footprint and using data-driven decision tools [1][2][3][4][5]. The idea of smart cities addresses how the advancement and unavoidable use of Information and Communication Technology (ICT) can impact urban development in regards to environmental, financial, and personal satisfaction aspects [6].…”

Section: Introductionmentioning

confidence: 99%

Vector Maps Mobile Application for Sustainable Eco-Driving Transportation Route Selection

2020

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The decisions managing all modes of transportation are currently based on the traffic rate and travel time. However, other factors such as Green House Gas (GHG) emissions, the sustainability index, fuel consumption, and travel costs are not considered. Therefore, more comprehensive methods need to be implemented to improve transportation systems and support users’ decision making in their daily commute. This paper addresses current challenges by utilizing data analytics derived from our proposed mobile application. The proposed application quantifies various factors of each transportation mode including but not limited to the cost, trip duration, fuel consumption, and Carbon Dioxide (CO2) emissions. All calculated travel costs are based on the real-time gas prices and toll fees. The users are also able to navigate to their destination and update the total travel costs in real-time. The emissions data per trip basis are aggregated to provide analytics of emissions usage. The traffic data is collected for the Southern California region and the effectiveness of the application is evaluated by twenty participants from California State University, Long Beach. The results demonstrate the application’s impacts on users’ decision-making and the propriety of the factors used in route selection. The proposed application can foster urban planning and operations vis-à-vis daily commutes, and as a result improve the citizens’ quality of life in various aspects.

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Building Energy Use Prediction Owing to Climate Change

Cited by 9 publications

References 8 publications

The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics

The Impact of Climate Change on a University Campus’ Energy Use: Use of Machine Learning and Building Characteristics

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Vector Maps Mobile Application for Sustainable Eco-Driving Transportation Route Selection

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