Large scale residential energy efficiency prioritization enabled by machine learning

Tarhuni, Badr Al; Naji, Adel; Brodrick, Philip G.; Hallinan, Kevin P.; Brecha, Robert J.; Yao, Zhen

doi:10.1007/s12053-019-09792-0

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…In this study, several two-story residences in the Midwest US were considered. The houses selected were from a group of residences owned by a university that had been recently audited to document the amount of insulation in the envelope [22]. The targeted residences in this study had variability relative to size, envelope R-Values, and sunlight exposure.…”

Section: Case Studymentioning

confidence: 99%

Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

2021

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Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences.

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Section: Case Studymentioning

confidence: 99%

Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

2021

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“…In the following, a summary of the related works is provided. Al Tarhuni et al [17] predicted each month's natural gas energy consumption based on integrating the physics-based approach and data energy system characteristics. Adding to that, meteorological data and historical energy consumption for all residences were used.…”

Section: Related Workmentioning

confidence: 99%

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

et al. 2021

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The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

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“…Papadopoulos and Kontokosta [110] use a gradient tree boosting method to develop a building energy performance grading method; this method has shown improved performance over linear models in predicting hourly and annual building energy use at the urban scale. Finally, Al Tarhuni et al [5] use random forest and deep learning neural network approaches to predict the monthly natural gas consumption of hundreds of university-owned student residences in the U.S. Midwest from readily accessible building geometry, energy system characteristics, and energy consumption data.…”

Section: Q3: Bottom-up/black-boxmentioning

confidence: 99%

“…5 In the absence of such reporting guidance, modeling techniques that fall in principle into the white-box quadrants of our classification may be perceived in practice to be black-box due to poor understanding of detailed model elements among researchers that are not part of the core model development team (due to too many equations, disparate input datasets, unclear variable relationships, etc. ).…”

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Developing a common approach for classifying building stock energy models

Langevin

Reyna

Ebrahimigharehbaghi

et al. 2020

Renewable and Sustainable Energy Reviews

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Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.

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Large scale residential energy efficiency prioritization enabled by machine learning

Cited by 16 publications

References 19 publications

Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Developing a common approach for classifying building stock energy models

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