| Summary: | 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>), 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.
|
|---|