A Rigorous Standalone Literature Review of Residential Electricity Load Profiles

Kewo, Angreine; Manembu, Pinrolinvic; Nielsen, Per Sieverts

doi:10.3390/en16104072

Cited by 3 publications

(4 citation statements)

References 84 publications

(333 reference statements)

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“…In addition, collected data can only describe a limited number of evaluation scenarios. Synthetic data generation can bridge these gaps by producing inexpensive, secure, and customizable building electricity load profiles that can describe multiple scenarios [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Magdaong,

Culaba,

Ubando

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

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Section: Introductionmentioning

confidence: 99%

“…Probabilistic and physics-based models are usually used for synthetic data generation [7]. Probabilistic models are easier to develop, however they can oversimplify systems.…”

Section: Introductionmentioning

confidence: 99%

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Magdaong,

Culaba,

Ubando

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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show abstract

“…Therefore, research on the modelling and analysis of residential electricity load profiles will contribute to the energy demand in specific areas: neighbourhoods, districts, cities, or regions and, specifically, the CITIES and PEDs research projects. One of the fundamental aims of these projects is to understand residential electricity consumption behaviour by synthesising local load profiles within cities [28,35]. The residential sector is of great importance, as it contributes to approximately 30% of the global demand for electricity [2].…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, load-shifting forms part of our research on DSM in the residential sector (Figure 1). In our previous works on synthesising residential electricity load profiles [28,[35][36][37], we found that appliances are mainly used to synthesize domestic load profiles. In accordance with what was revealed in [15], there is a need to incorporate the consumption patterns of electrical appliances into DSM models.…”

Section: Introductionmentioning

confidence: 99%

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Manembu,

Kewo,

Bramstoft

et al. 2023

Energies

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Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.

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A Review of Residential Electricity Load Shifting at the Appliance Level

Manembu,

Kewo,

Bramstoft

et al. 2023

Preprint

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Load shifting is one of the demand side management (DSM) strategies to support the efficiency in the electricity grid during the peak hours demand. Load shifting at the appliance level is interesting to review since the appliance usage is one of the main inputs in the load profile analysis. More review studies in load shifting at the appliance level are required as it is a specific issue of load profile research body, and still limited studies are available at this moment. It is also essential to focus on the appliance usage patterns to better understand the impacts and characteristics of the individual appliance. The existing studies in load shifting have applied common structured literature reviews, and our study addresses the transparency of each stage and sub-stage of the selection of the final list studies. The finding shows that efficiency has been achieved in the installed capacity reduction, cost including emission reductions, and peak consumption reduction. The most applied method in the load shifting at the appliance level is by developing the load shifting optimisation algorithms. This work contributes to provide a transparent process of applying a systematicity literature review, where it becomes a source of knowledge and grounded theory. It contributes to a limited and specific research of load shifting at the appliance level by highlighting and discussing the key findings for the readers. In specific, it contributes to improve energy efficiency by presenting the load shifting methods at the appliance level and identifying the controllable and uncontrollable appliances. This detail review study at the appliance level can make valuable contributions to support decision and policy-making by illuminating new dynamic system in load shifting area in specific, and demand side management in general for energy efficiency.

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A Rigorous Standalone Literature Review of Residential Electricity Load Profiles

Cited by 3 publications

References 84 publications

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

A Review of Residential Electricity Load Shifting at the Appliance Level

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