A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Microgrids have recently emerged as a building block for smart grids combining distributed
renewable energy sources (RESs), energy storage devices, and load management …
renewable energy sources (RESs), energy storage devices, and load management …
Load forecasting techniques and their applications in smart grids
The growing success of smart grids (SGs) is driving increased interest in load forecasting
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …
A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting
L Fang, B He - Applied Energy, 2023 - Elsevier
Accurate energy load forecasting can not only provide favorable conditions for ensuring
energy security but also reduce carbon emissions and thereby slow down the process of …
energy security but also reduce carbon emissions and thereby slow down the process of …
Towards developing a systematic knowledge trend for building energy consumption prediction
The rapid depletion of natural sources of energy, coupled with increasing global population
has triggered the emergence of various techniques and strategies for building energy …
has triggered the emergence of various techniques and strategies for building energy …
Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid
The evolution of advanced metering infrastructure (AMI) has increased the electricity
consumption data in real-time manifolds. Using this massive data, the load forecasting …
consumption data in real-time manifolds. Using this massive data, the load forecasting …
Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting
With the booming growth of advanced digital technologies, it has become possible for users
as well as distributors of energy to obtain detailed and timely information about the electricity …
as well as distributors of energy to obtain detailed and timely information about the electricity …
[HTML][HTML] A novel short-term household load forecasting method combined BiLSTM with trend feature extraction
K Wu, X Peng, Z Chen, H Su, H Quan, H Liu - Energy Reports, 2023 - Elsevier
Aiming to reduce the short-term household load prediction error caused by small load scale
and differently residential electricity consumption behavior, a novel hybrid forecasting model …
and differently residential electricity consumption behavior, a novel hybrid forecasting model …
Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies
Optimal behind-the-meter energy management often requires a day-ahead electric load
forecast capable of learning non-linear and non-stationary patterns, due to the spatial …
forecast capable of learning non-linear and non-stationary patterns, due to the spatial …
An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems
Smart home energy management (SHEM) with residential photovoltaic (PV)-battery systems
is a complicated issue with different facets. An integrated SHEM model covering the …
is a complicated issue with different facets. An integrated SHEM model covering the …
Towards modified entropy mutual information feature selection to forecast medium-term load using a deep learning model in smart homes
Over the last decades, load forecasting is used by power companies to balance energy
demand and supply. Among the several load forecasting methods, medium-term load …
demand and supply. Among the several load forecasting methods, medium-term load …