Modeling energy demand—a systematic literature review
PA Verwiebe, S Seim, S Burges, L Schulz… - Energies, 2021 - mdpi.com
In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …
published between 2015 and 2020, is presented. This provides researchers with an …
Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …
response in power systems. Electrical loads are characterized by volatility and uncertainty …
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
As the volatility of electricity demand increases owing to climate change and electrification,
the importance of accurate peak load forecasting is increasing. Traditional peak load …
the importance of accurate peak load forecasting is increasing. Traditional peak load …
Artificial intelligence and statistical techniques in short-term load forecasting: a review
Electrical utilities depend on short-term demand forecasting to proactively adjust production
and distribution in anticipation of major variations. This systematic review analyzes 240 …
and distribution in anticipation of major variations. This systematic review analyzes 240 …
Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model
GF Fan, LL Peng, WC Hong - Applied energy, 2018 - Elsevier
Short term load forecasting (STLF) is an important issue for an electricity power system, to
enhance its management efficiency and reduce its operational costs. However, STLF is …
enhance its management efficiency and reduce its operational costs. However, STLF is …
Future power distribution grids: Integration of renewable energy, energy storage, electric vehicles, superconductor, and magnetic bus
This paper focuses on a review of the state of the art of future power grids, where new and
modern technologies will be integrated into the power distribution grid, and will become the …
modern technologies will be integrated into the power distribution grid, and will become the …
Forecasting residential electricity consumption using the novel hybrid model
GF Fan, Y Zheng, WJ Gao, LL Peng, YH Yeh… - Energy and …, 2023 - Elsevier
The accuracy of power load forecasting plays an important role in the development of the
economy and the promotion of energy consumption and transformation. Aiming at the strong …
economy and the promotion of energy consumption and transformation. Aiming at the strong …
Forecasting seasonal demand for retail: A Fourier time-varying grey model
Seasonal demand forecasting is critical for effective supply chain management. However,
conventional forecasting methods face difficulties accurately estimating seasonal variations …
conventional forecasting methods face difficulties accurately estimating seasonal variations …
A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting
S Yi, H Liu, T Chen, J Zhang… - … Transmission & Distribution, 2023 - Wiley Online Library
Numerous studies on short‐term load forecasting (STLF) have used feature extraction
methods to increase the model's accuracy by incorporating multidimensional features …
methods to increase the model's accuracy by incorporating multidimensional features …
A hybrid modelling method for time series forecasting based on a linear regression model and deep learning
W Xu, H Peng, X Zeng, F Zhou, X Tian, X Peng - Applied Intelligence, 2019 - Springer
Time series forecasting has important theoretical significance and engineering application
value. A number of studies have shown that hybrid modelling is very successful in various …
value. A number of studies have shown that hybrid modelling is very successful in various …