Abstract: Deep learning, as an increasingly powerful and popular data analysis tool, has the potential to improve smart grid operation.
Abstract—Deep learning, as an increasingly powerful and popular data analysis tool, has the potential to improve smart grid operation.
The ML-based model maintains the system performance in an efficient manner and steers the power to critical loads during adverse and unfavorable environments. .
Mar 18, 2024 · In this paper, we have primarily addressed the two significant issues of model optimization and electricity consumption forecasts. Future ...
The proposed one-dimensional CNNs, LSTM and GRU variants are applied to real-world electricity load data for 1-hour-ahead and 24-hour-ahead prediction tasks.
An Insight of Deep Learning Based Demand Forecasting in Smart Grids
pmc.ncbi.nlm.nih.gov › PMC9921606
This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids.
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May 26, 2022 · We propose in this article a flexible hybrid machine learning model that can be used to make predictions of different ranges by using both standard neural ...
Dec 19, 2023 · This paper discusses the intermittent online sales and proposes an AI-based model for forecasting demand.
Feb 28, 2024 · This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption.
In this study, the electricity demands of some Fortune 500 companies in Türkiye have been forecasted by using deep learning techniques.
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