A Residential End Use Energy Consumption and Appliance Ownership Patterns in India

Walia, Archana; Tathagat, Tanmay; Dhingra, Neha; Varma, Piyush

doi:10.1007/978-3-030-79124-7_6

Cited by 7 publications

(2 citation statements)

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“…A lot of factors were considered in categorizing the studied literature such as; appliance type used in the studies, the input parameters, the algorithm type, the algorithms detection level (where the algorithms is implemented), the computing platform the algorithm is implemented and the final output of the model [9,15,32,26,31,34]. This is simplified in Table 3 as illustrated.…”

Section: Categorization Of Literaturementioning

confidence: 99%

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

Rauf

Adekoya

2023

Journal of Electrical Systems and Inf Technol

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The demand for electricity at home has increased in recent times globally, this high demand for continuous, stable and affordable power can be attributed to the demand for comfortable lifestyle of consumers but the quality and efficiency of the appliances being used remain questionable. Malfunctioning appliances usually show a power signature statistically different from their normal behavior, which can lead to higher energy consumption or more serious damages. As a result, numerous studies in recent times have been conducted on the household electrical appliance anomaly behaviors to find the root-cause of these anomalies using machine learning techniques and algorithms. This study attempted to undertake a systematic and critical review of ninety-two (92) research works reported in academic journals over fifteen (15) years (2006–2021) in the area of household electrical appliance anomaly detections and knowledge extraction using machine learning. The various techniques used in these reports were clustered based on machine learning-based techniques, statistical techniques and physical based approach techniques and the parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. This clustering was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modeling. For the number of data source used, the results revealed that 81.2% of documents reviewed used single sources and Autoregressive integrated moving average (ARIMA) was the highest implemented regression model (60.9%), the probability model that was mostly implemented was the Bayesian network. Furthermore, the study revealed that, root-mean-square error (RMSE) accounted 35% was the most used error metric among household appliance abnormal behaviors, followed by mean absolute percentage error MAPE which accounted 32%. The study further revealed that 46% of appliance abnormal detections was based on weather parameters, and historical energy consumption. Finally, we recap the challenges and limitations for further research in electrical appliance anomaly detections locally and globally.

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Section: Categorization Of Literaturementioning

confidence: 99%

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

Rauf

Adekoya

2023

Journal of Electrical Systems and Inf Technol

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“…However, increasing urbanization has led to migration, owing to which rental dwellings are on the rise. Affluence, status,and need for comfort and convenience (Sovacool, 2011) have increased appliance usage in rented homes (Walia et al, 2015) as well.…”

Section: Data Analysis and Key Findingsmentioning

confidence: 99%

Residential Consumer Understanding of Electricity Bills

Joshi

Sen

2021

International Journal of Asian Business and Information Management

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The neoclassical model of economics assumes that greater the information available, the easier it is for a consumer to make decisions. In the context of electricity usage, overwhelming amounts of information presented on energy bills and a lack of real-time information deter consumers from understanding their own energy usage and developing personalized strategies to reduce it. This study assesses the literacy levels regarding electricity bills amongst consumers in India. Regression results indicate that homes with stronger beliefs in energy conservation minimize their usage. The size of the dwelling,demographics, and the ownership of the dwelling influence literacy levels regarding electricity bills. The study had a counterintuitive finding, as the respondents were found reluctant to receive a normative comparison of their electricity bills—strange for a collectivistic cultured society like India. The study recommends making electricity bills more visually appealing and increasing their frequency to increase awareness of energy usage.

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Factors affecting household’s use of energy-saving appliances in Sri Lanka: an empirical study using a conceptualized technology acceptance model

et al. 2023

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A Residential End Use Energy Consumption and Appliance Ownership Patterns in India

Cited by 7 publications

References 0 publications

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

Residential Consumer Understanding of Electricity Bills

Factors affecting household’s use of energy-saving appliances in Sri Lanka: an empirical study using a conceptualized technology acceptance model

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