Abstract:
As load forecasting nowadays is a crucial and integral part of the energy
production procedures a large number of forecasting methods has been pro-
posed to address it. However, although there are many forecasting methods
which take into account the advances in information, metering and control
technologies in order to address the challenges of forecasting problems, the
accuracy and efficiency levels required for each type of applications are yet
to be determined. Technologies such as machine learning techniques have
been proven useful for short-term electricity load forecasting especially in
microgrids where a large variety of data should be included in the energy
consumption prognosis. In this paper, we present an overview of the main
machine learning algorithms applied to electricity load datasets for short-term
forecasting such as Support Vector Machines (SVM), k-Nearest Neighbors
(kNN), Random Forest and Artificial Neural Networks (ANN) and compare
their performance efficiency, capabilities and limitations.
CITATION:
IEEE format
E. Mele, “A Review of Machine Learning Algorithms Used for Load Forecasting at Microgrid Level,” in Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research, Belgrade, Singidunum University, Serbia, 2019, pp. 452-458. doi:10.15308/Sinteza-2019-452-458
APA format
Mele, E. (2019). A Review of Machine Learning Algorithms Used for Load Forecasting at Microgrid Level. Paper presented at Sinteza 2019 - International Scientific Conference on Information Technology and Data Related Research. doi:10.15308/Sinteza-2019-452-458