Can smart energy information interventions help householders save electricity? A SVR machine learning approach

Wang, Andong; Lam, Jacqueline C.K.; Song, Shiguang; Li, Victor O. K.; Guo, Peiyang

doi:10.1016/j.envsci.2020.07.003

Cited by 21 publications

(15 citation statements)

References 21 publications

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“…The studies that do not use a follow-up often use consumption feedback [15,[53][54][55][56][57]] and increasing consumption literacy [9,56,58,59] as means through which to encourage conservation behavior. The mechanisms behind consumption feedback and consumption literacy are similar.…”

Section: Maintained Behavioral Changementioning

confidence: 99%

Determinants of Long-Term Water and Energy Conservation Behavior: An Integrated Review

Ambaum,

Corten,

Lambooij

et al. 2024

Sustainability

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Over the last decades, drinking water and energy use have increased exponentially. To preserve ecosystems in the long term, a change in behavior is necessary on all levels of society including on the household level. This paper presents an integrated review of the determinants of long-term drinking water and energy conservation behavior of households. We identified forty-nine relevant studies discussing long-term conservation behavior in the context of drinking water and energy use. Long-term conservation behavior was measured as either persistent behavior, maintaining behavioral change, or intentions to maintain behavior, each with specific determinants. We found four key factors for long-term conservation behavior: consumption feedback, household characteristics, effort, and motives for conservation behavior. For future studies, we suggest follow-up questionnaires or interviews to measure the persistence of behavior and differentiate between curtailment and efficiency behavior. Worthwhile avenues for future research on long-term conservation behavior are household-tailored feedback mechanisms and the interaction between contextual factors and effort-based choices.

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Section: Maintained Behavioral Changementioning

confidence: 99%

Determinants of Long-Term Water and Energy Conservation Behavior: An Integrated Review

Ambaum,

Corten,

Lambooij

et al. 2024

Sustainability

View full text Add to dashboard Cite

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“…Froemelt et al [37] Neural network Guo et al [38] Support Vector Machine Wang et al [39] Artificial neural network Shams Amiri et al [40] To bridge this knowledge gap, this study firstly investigates the importance of factors from aspects of housing conditions, economic income, demographic structure, household appliances, and energy use habits on household carbon emissions based on the household survey data in Japan. We use machine learning methods including LASSO, Decision Tree, Random Forest and XGBoost to precisely identify driving factors, which fit well with our multi-dimensional data.…”

Section: Random Forestmentioning

confidence: 99%

“…In order to infer the overall picture of household carbon emissions, machine learning methods are also useful to estimate based on the recognized patterns and rules from surveyed households when given strong driving factors. But, limited by the data quality, few households' energy survey support the basic data demand of machine learning, only limited interests tries have been made in household electricity consumption of Hong Kong [39], household transportation energy consumption in Delaware Valley region [40] and consumption-induced environmental impacts in Switzerland [37]. The conclusions derived from literature review are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Exploring household emission patterns and driving factors in Japan using machine learning methods

Chen

Zhong

et al. 2022

Applied Energy

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“…Many research works have demonstrated promising results in detecting anomalous energy consumption behaviors using artificial intelligence (AI) [3]. For example, various machine learning models were investigated to enhance energy efficiency in common households [4,5]. Most of the efforts were made to detect abnormal power consumption, which was often related to the malfunction of appliances.…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

Cheng

Chaw

Goh

et al. 2022

Sensors

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The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

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Can smart energy information interventions help householders save electricity? A SVR machine learning approach

Cited by 21 publications

References 21 publications

Determinants of Long-Term Water and Energy Conservation Behavior: An Integrated Review

Determinants of Long-Term Water and Energy Conservation Behavior: An Integrated Review

Exploring household emission patterns and driving factors in Japan using machine learning methods

Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

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