Review of family-level short-term load forecasting and its application in household energy management system
P Ma, S Cui, M Chen, S Zhou, K Wang - Energies, 2023 - mdpi.com
With the rapid development of smart grids and distributed energy sources, the home energy
management system (HEMS) is becoming a hot topic of research as a hub for connecting …
management system (HEMS) is becoming a hot topic of research as a hub for connecting …
A review of distribution network applications based on smart meter data analytics
The large-scale roll-out of smart meters allows the collection of a vast amount of fine-grained
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
electricity consumption data. Once analyzed, such data can enable cutting-edge data-driven …
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 …
A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine
C Liu, B Sun, C Zhang, F Li - Applied energy, 2020 - Elsevier
Residential electricity consumption accounts for a large proportion of the primary energy
consumption in China. Building energy management can effectively improve energy …
consumption in China. Building energy management can effectively improve energy …
Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms
Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both
electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed …
electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed …
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
Y Dong, X Ma, T Fu - Applied Soft Computing, 2021 - Elsevier
Deep learning approaches have shown superior advantages than shallow techniques in the
field of electrical load forecasting; however, their applications in existing studies encounter …
field of electrical load forecasting; however, their applications in existing studies encounter …
Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM
Load forecasting in power microgrids and load management systems is still a challenge and
needs an accurate method. Although in recent years, short-term load forecasting is done by …
needs an accurate method. Although in recent years, short-term load forecasting is done by …
A prediction approach with mode decomposition-recombination technique for short-term load forecasting
W Yue, Q Liu, Y Ruan, F Qian, H Meng - Sustainable Cities and Society, 2022 - Elsevier
Short-term load forecasting (STLF) is critical for ensuring smooth and efficient functioning of
power systems. In this study, a prediction approach, combining ensemble empirical mode …
power systems. In this study, a prediction approach, combining ensemble empirical mode …
A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution
Improving the quality of Wind Speed Interval prediction is important to maximize the usage of
integrated wind energy as well as to reduce the adverse effects of the uncertainties …
integrated wind energy as well as to reduce the adverse effects of the uncertainties …
Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads
Buildings are one of the significant sources of energy consumption and greenhouse gas
emission in urban areas all over the world. Lighting control and building integrated …
emission in urban areas all over the world. Lighting control and building integrated …