A Probabilistic Modelling Approach for Residential Load Profiles

Ziegler, Felix; Seim, Stephan; Verwiebe, Paul; Müller-Kirchenbauer, Joachim

doi:10.5281/zenodo.3689339

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…There has been substantial study into the connection Innovation Forever Publishing Group J Mod Green Energy 2022; 1: 6 5/25 https://www.doi.org/10.53964/jmge.2022006 between the possession of appliances (i.e., number and varieties of household appliances) and the use of electricity. Several scholars agree that the number of appliances has a substantial effect on domestic demand for electricity use [23][24][25] . As researched by Yu et al [26] , found that the number of electrical appliances increases with residential buildings.…”

Section: Categories Of Household Electrical Appliancementioning

confidence: 99%

Using Household Electricity Metrics in Identifying Trends in Residential Appliances Usage in Selected Ghanaian Homes

2022

J Mod Green Energy

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Objective: The paper aims to clarify the association between consumption trends for four appliance types and power consumption in residential facilities in selected buildings in Ghana. It proposes modelling the consumers' consumption demand process and outlining why and how appliance use in residential homes is important throughout the energy supply system and consumer expectations for improved energy quality process. Integrating the various aspects of saving electricity, this analysis provides a comprehensive overview of the factors that contribute to successful energy-saving behaviour the study seeks to recognize the need for a comprehensive approach to achieving energy reduction targets, incorporating household dynamics and behaviors to identify the root causes of energy-saving behavior than is normally found in the home appliance consumption trends in literature. Methods:The residential household survey dataset fulfilled the research criteria by analyzing data from 100 households and about 1007 individual household electrical appliances. The tracked data includes accurate data of different electrical appliances used in the selected homes, and consumption data over a duration of one year. Several computer programming languages and software methods have been used to build the stochastic demand model for the trend analysis. Anaconda software especially the RStudio and MATLAB are used to define indicators of customer behaviour, analyze the dataset for the chosen home appliances and examine the impact of appliance use on the household dataset. The model (household types, appliance ownership, and electricity demand) has been introduced in the MATLAB language. The agent-based model is introduced to capture realistic individual-level behavior patterns and coordinated reactive changes in human behavior in order to better predict the reduction dynamics of consumption of electricity in residential homes. Results:The paper provides empirical evidence on how behavioral changes through the study of appliances help reduce household electricity. The results showed that the consumption behavior of devices for individuals is correlated with each other. The refrigerators had the most switch-on events in a day, and the refrigerator had the most power-ups in a day. This happens because the compressor works continuously Innovation Forever Publishing Group

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Section: Categories Of Household Electrical Appliancementioning

confidence: 99%

Using Household Electricity Metrics in Identifying Trends in Residential Appliances Usage in Selected Ghanaian Homes

2022

J Mod Green Energy

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“…Stochastic, fuzzy and grey techniques can be implemented as stand-alone models [64], e.g., simulating load profiles by sampling from stochastic processes such as Gaussian processes or Markov chains [52,65,66]. However, the representation of uncertain outcomes through stochastic, fuzzy or grey expressions is often combined with other techniques such as statistical regression [67,68] or ANN [69,70], giving results in the form of membership functions, intervals or probabilistic density.…”

Section: Techniques and Input Datamentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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A Probabilistic Modelling Approach to Analyze the Impact of Electric Vehicles on Residential Load Profiles

Huang

Ziegler

2021

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

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A Probabilistic Modelling Approach for Residential Load Profiles

Cited by 3 publications

References 15 publications

Using Household Electricity Metrics in Identifying Trends in Residential Appliances Usage in Selected Ghanaian Homes

Using Household Electricity Metrics in Identifying Trends in Residential Appliances Usage in Selected Ghanaian Homes

Modeling Energy Demand—A Systematic Literature Review

A Probabilistic Modelling Approach to Analyze the Impact of Electric Vehicles on Residential Load Profiles

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