Forecasting residential electricity consumption: a bottom-up approach for Brazil by region

Maçaira, Paula; Elsland, Rainer; Oliveira, Fernando; Souza, Reinaldo Castro; Fernandes, Gláucia

doi:10.1007/s12053-020-09860-w

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Moreover, at the building level, ELF finds applications in the optimal operation of both electric and thermal energy storage units, as well as the building's Heating, Ventilation, and Air Conditioning (HVAC) system [29]. In the event that ELF and, consequently, DSM [4,5] result in a reduction in energy consumption, there are also environmental benefits (i.e., fewer emissions) and financial ones [6] for building managers or even for end-users or tenants.…”

Section: Load Forecastingmentioning

confidence: 99%

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Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Mystakidis,

Koukaras,

Tsalikidis

et al. 2024

Energies

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Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between generation and consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, and consumers to manage energy resources effectively and make educated decisions about energy generation and consumption, EF is essential. For many applications, such as Energy Load Forecasting (ELF), Energy Generation Forecasting (EGF), and grid stability, accurate EF is crucial. The state of the art in EF is examined in this literature review, emphasising cutting-edge forecasting techniques and technologies and their significance for the energy industry. It gives an overview of statistical, Machine Learning (ML)-based, and Deep Learning (DL)-based methods and their ensembles that form the basis of EF. Various time-series forecasting techniques are explored, including sequence-to-sequence, recursive, and direct forecasting. Furthermore, evaluation criteria are reported, namely, relative and absolute metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2), and Coefficient of Variation of the Root Mean Square Error (CVRMSE), as well as the Execution Time (ET), which are used to gauge prediction accuracy. Finally, an overall step-by-step standard methodology often utilised in EF problems is presented.

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Section: Load Forecastingmentioning

confidence: 99%

“…One benefit is a decline in energy use, which has favourable environmental effects, such as lower emissions. As noted in [6], there are also monetary advantages for building managers, end users, or tenants. EF is also essential for supporting efficient energy management and decision-making procedures.…”

Section: Introductionmentioning

confidence: 99%

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Mystakidis,

Koukaras,

Tsalikidis

et al. 2024

Energies

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“…Engineering-based techniques stand out in having a clear tendency towards the building and appliance level, reflecting the high level of detail which is characteristic of this technique. Scaling an engineering-based model to the national level requires high amounts of detailed technical system data and has only been done in a few cases [126]. In contrast, statistical techniques are used most frequently on large geographic scales, reflecting their traditional role in econometric analyses.…”

Section: Spatiotemporal Level Of Detailmentioning

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 study on the prediction of electricity consumption considering the energy efficiency measures—applied in case of the Brazilian public sector

Filgueiras,

Coelho da Silva

2023

Energy Efficiency

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Forecasting residential electricity consumption: a bottom-up approach for Brazil by region

Cited by 8 publications

References 38 publications

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Modeling Energy Demand—A Systematic Literature Review

A study on the prediction of electricity consumption considering the energy efficiency measures—applied in case of the Brazilian public sector

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