Leveraging forecasting techniques for power procurement and improving grid stability: A strategic approach

Mahendra, Lagineni; Kalluri, Rajesh; Rao, M. Siddharth; Kumar, R. K. Senthil; Bindhumadhava, B. S.

doi:10.1109/appeec.2017.8308978

Cited by 4 publications

(1 citation statement)

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“…It gives SGs the necessary flexibility, enabling power distributors to efficiently coordinate and control future energy input and output. It helps energy suppliers to precisely forecast energy usage trends and be prepared for increased power demands [7], anticipating and responding to sudden demand spikes to ensure grid stability [8].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%