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Electricity forecasting on the individual household level enhanced based on activity patterns.

ABSTRACT: Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

SUBMITTER: Gajowniczek K

PROVIDER: S-EPMC5396872 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Electricity forecasting on the individual household level enhanced based on activity patterns.

Gajowniczek Krzysztof K Ząbkowski Tomasz T

PloS one 20170419 4

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents' daily activities and appliance usages on the power consumption of the entire ...[more]

PMID: 28423039

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Project description:IntroductionPeople living in the same area share similar determinants of infant mortality, such as access to healthcare. The community's prevailing norms and attitudes about health behaviours could also influence the health care decisions made by individuals. In diversified communities like Ethiopia, differences in child health outcomes might not be due to variation in individual and family characteristics alone, but also due to differences in the socioeconomic characteristics of the community where the child lives. While individual level characteristics have been examined to some extent, almost all studies into infant mortality conducted in Ethiopia have failed to consider the impact of community-level characteristics. Therefore, this study aims to identify individual and community level determinants of infant mortality in Ethiopia.MethodData from the Ethiopian Demographic and Health Survey in 2016 were used for this study. A total of 10641 live births were included in this analysis. A multi-level logistic regression analysis was used to examine both individual and community level determinants while accounting for the hierarchal structure of the data.ResultsIndividual-level characteristics such as infant sex have a statistically significant association with infant mortality. The odds of infant death before one year was 50% higher for males than females (AOR = 1.66; 95% CI: 1.25-2.20; p-value <0.001). At the community level, infants from pastoralist areas (Somali and Afar regions) were 1.4 more likely die compared with infants living in the Agrarian area such as Amhara, Tigray, and Oromia regions; AOR = 1.44; 95% CI; 1.02-2.06; p-value = 0.039).ConclusionIndividual, household and community level characteristics have a statistically significant association with infant mortality. In addition to the individual based interventions already in place, household and community-based interventions such as focusing on socially and economically disadvantaged regions in Ethiopia could help to reduce infant mortality.

Dataset Information

Electricity forecasting on the individual household level enhanced based on activity patterns.

Publications

Electricity forecasting on the individual household level enhanced based on activity patterns.

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