Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks
Excessive Power Consumption (PC) and demand for power is increasing on a daily basis,
due to advancements in technology, the rise in electricity-dependent machinery, and the …
due to advancements in technology, the rise in electricity-dependent machinery, and the …
[HTML][HTML] Load forecasting with machine learning and deep learning methods
M Cordeiro-Costas, D Villanueva, P Eguía-Oller… - Applied Sciences, 2023 - mdpi.com
Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …
buildings without any structural change and is the basis for controlling and optimizing …
Deep learning based densely connected network for load forecasting
Load forecasting is of crucial importance for operations of electric power systems. In recent
years, deep learning based methods are emerging for load forecasting because their strong …
years, deep learning based methods are emerging for load forecasting because their strong …
[HTML][HTML] Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review
R Mathumitha, P Rathika, K Manimala - Artificial Intelligence Review, 2024 - Springer
Urbanization increases electricity demand due to population growth and economic activity.
To meet consumer's demands at all times, it is necessary to predict the future building …
To meet consumer's demands at all times, it is necessary to predict the future building …
[HTML][HTML] Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy
Smart grid technology based on renewable energy and energy storage systems are
attracting considerable attention towards energy crises. Accurate and reliable model for …
attracting considerable attention towards energy crises. Accurate and reliable model for …
Deep learning based short term load forecasting with hybrid feature selection
SS Subbiah, J Chinnappan - Electric Power Systems Research, 2022 - Elsevier
The reliable and an economic operation of the power system rely on an accurate prediction
of short term load. In this paper, a deep learning based Long Short Term Memory (LSTM) …
of short term load. In this paper, a deep learning based Long Short Term Memory (LSTM) …
Split federated learning for 6G enabled-networks: Requirements, challenges and future directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart
services and innovative applications. Such a context urges a heavy usage of Machine …
services and innovative applications. Such a context urges a heavy usage of Machine …
[HTML][HTML] Short-term load forecasting using convolutional neural networks in COVID-19 context: the Romanian case study
Short-term load forecasting (STLF) is fundamental for the proper operation of power
systems, as it finds its use in various basic processes. Therefore, advanced calculation …
systems, as it finds its use in various basic processes. Therefore, advanced calculation …
Short term load forecasting with markovian switching distributed deep belief networks
In modern power systems, centralised short term load forecasting (STLF) methods raise
concern on high communication requirements and reliability when a central controller …
concern on high communication requirements and reliability when a central controller …
[HTML][HTML] Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler
Electrical load forecasting provides knowledge about future consumption and generation of
electricity. There is a high level of fluctuation behavior between energy generation and …
electricity. There is a high level of fluctuation behavior between energy generation and …