The results indicate that the load forecasting models developed provide more accurate forecasts compared to the conventional backpropagation network forecasting ...
Neural Networks are currently finding practical applications, ranging from 'soft' regulatory control in consumer products.
The results indicate that the load forecasting models developed provide more accurate forecasts compared to the conventional backpropagation network forecasting ...
Improved neural networks based on short-term electric load forecasting models for the Power System of the Greek Island of Crete are presented and the ...
The results indicate that the load-forecasting models developed in this way provide more accurate forecasts, compared with conventional backpropagation network ...
This paper conducts a benchmark study of several machine learning methods to compare their ability to determine the most significant weather-related variables ...
Sep 20, 2024 · This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting.
Apr 29, 2022 · The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy ...
Aug 10, 2024 · Deep learning is a more advanced learning paradigm in the machine learning ... Energy Consumption Forecasting Using Ensemble Learning Algorithms.
Sep 13, 2021 · In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared.
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