Ensemble Forecasting of Monthly Electricity Demand Using Pattern Similarity-Based Methods

Pełka, Paweł; Dudek, Grzegorz

doi:10.1007/978-3-030-61401-0_66

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…MTF for power demand is a thoroughly studied topic. Numerous approaches have been taken to resolve this problem, including similarity-based approaches [56][57][58] and classical/statistical methods [59,60]. Initially, conventional methods were used to forecast the amount of electricity consumed.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Mystakidis,

Koukaras,

Tsalikidis

et al. 2024

Energies

View full text Add to dashboard Cite

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“…P. Pełka et al presented a hybrid framework has been successfully associated to estimate power consumption for residential users. The authors claim that based on the results of this method, the monthly electricity demand can be predicted and the use of resources can be adjusted to suit the needs [180]. S. Motepe et al composed a novel AI-based hybrid approach for STLF in South Africa and achieved the expected results.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A review on short‐term load forecasting models for micro‐grid application

Kondaiah

Saravanan

Sanjeevikumar

et al. 2022

The Journal of Engineering

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Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role throughout the operation of the conventional power system. The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives. This article presents a survey of the latest analytical and approximation techniques reported in the literature to model STLF in an MG environment. This article mainly focusses on the review on important methods applied to forecast renewable energy availability, energy demand, and price and load demand. Different models, their main objectives, methodology, error percentage, and so forth, are critically reviewed and analysed. For quick reference, we have highlighted the important points in the form tables. The researchers can quickly identify and frame their research problem related to the LF area by reading this review paper.

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Ensemble Forecasting of Monthly Electricity Demand Using Pattern Similarity-Based Methods

Cited by 2 publications

References 18 publications

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

A review on short‐term load forecasting models for micro‐grid application

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