Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability

Pham, Anh-Duc; Ngo, Ngoc-Tri; Truong, Thi Thu Ha; Huynh, Nhat-To; Truong, Ngoc-Son

doi:10.1016/j.jclepro.2020.121082

Cited by 209 publications

(83 citation statements)

References 23 publications

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“…The concept of a green culture is helping to forge a productive way of tackling environmental issues (Pham, 2018;Kucukoglu, 2018), and this motivates employees to find ways of minimizing the impact on the environment (Roscoe, 2019). The sustainability actions of an hotel not only help to reduce air, water and land pollution but enhanced customer and employee satisfaction levels can lead to improved profitability (Pham, 2020;Newton and Manins, 2019).…”

Section: Green Organizational Culturementioning

confidence: 99%

Analyzing employee perspectives on the impact of COVID-19 on sustainable practices: a study of five-star hotels in India

Mehta

Sharma

2021

WHATT

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PurposeGiven the pace of global environmental degradation, companies and individuals alike are exploring ways and means of protecting the environment. In this context, the attitudes of hoteliers and their employees toward sustainability are key to the successful implementation of these practices. This paper aims to consider the impact of attitude development and COVID-19 on the sustainability performance of hotels. The study also explores contributions made by hotels to environmental sustainability and society more generally.Design/methodology/approachThe study is based on interviews and survey questionnaires completed by employees of five-star hotels in India, and qualitative methods were used to process the data.FindingsThe findings of this study confirm the devastating impact of COVID-19 on both economic and societal sustainability in what otherwise would be a profitable sector of the economy.Practical implicationsThe study has implications for hoteliers, the government, environmental agencies and for employees and could assist with the formulation of recovery packages by government and in the development of new standard operating procedures to enable hotels to step-up on the self-recovery path.Originality/valueThe paper provides an analysis of the direct effects of the pandemic on financial sustainability and its mediating impact on the efforts of hotels to attain sustainable environment.

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Section: Green Organizational Culturementioning

confidence: 99%

Analyzing employee perspectives on the impact of COVID-19 on sustainable practices: a study of five-star hotels in India

Mehta

Sharma

2021

WHATT

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“…Second, explanatory feedback through simulations of future scenarios using machine learning. Adaptive algorithms will improve the effectiveness of future scenarios, through better forecasting and simulations, that are presented [80,81]. More insight will be created when future effects of behavior changes are presented.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Competition in a Household Energy Conservation Game

et al. 2021

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This paper presents the results of a game study, comparing Powersaver Game including a competition feature versus the same game excluding a competition feature with respect to energy conservation in the household. In a pretest–posttest design, we tested whether change in attitude, knowledge and behavior with respect to energy conservation in the household was different for participants playing Powersaver Game with or without competition. All energy conservation activities that the application provides (e.g., washing clothes at low temperatures) take place in the real world and feedback is based on real-time energy consumption. This so-called reality-enhanced game approach aims to optimize the transfer between the game world and the real world. Household energy consumption changed significantly and positively in the long term due to competition. A significant difference of 8% in energy consumption between both conditions after the intervention was detected. Besides energy conservation, no further differences were detected between conditions. The chain of events, that an increase in knowledge leads to attitude change, which in turn results in behavior change in the long term is confirmed by means of a path analysis. We conclude that Powersaver Game is effective in the transfer of energy conservation knowledge, which leads to energy saving behavior in the long term while competition additionally contributes to more change in behavior.

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“…The use of machine learning (ML) has been applied to different industries such as sustainability of materials [ 36 ], techno-economics [ 37 ], molecular crystals engineering [ 38 ], energy [ 39 ], diagnostics in medicine [ 40 ] and, more recently, food/beverages [ 17 , 18 , 22 , 29 , 41 ] and agriculture [ 42 , 43 , 44 ]. This has been an effective tool to aid in the prediction and rapid assessment of products; however, a common issue found when using ML is the overfitting of the models because the generalization of the data is not achieved.…”

Section: Introductionmentioning

confidence: 99%

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

Viejo

Tongson

Fuentes

2021

Sensors

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Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of six commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.

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Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability

Cited by 209 publications

References 23 publications

Analyzing employee perspectives on the impact of COVID-19 on sustainable practices: a study of five-star hotels in India

Analyzing employee perspectives on the impact of COVID-19 on sustainable practices: a study of five-star hotels in India

Competition in a Household Energy Conservation Game

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

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