A hybrid short-term load forecasting approach for individual residential customer
This article proposes a hybrid method (HM) to improve the accuracy of short-term individual
residential load forecasting. The HM includes an ensemble model (EM), deep ensemble …
residential load forecasting. The HM includes an ensemble model (EM), deep ensemble …
Hybrid CNN-LSTM model for short-term individual household load forecasting
M Alhussein, K Aurangzeb, SI Haider - Ieee Access, 2020 - ieeexplore.ieee.org
Power grids are transforming into flexible, smart, and cooperative systems with greater
dissemination of distributed energy resources, advanced metering infrastructure, and …
dissemination of distributed energy resources, advanced metering infrastructure, and …
A novel short-term residential electric load forecasting method based on adaptive load aggregation and deep learning algorithms
T Hou, R Fang, J Tang, G Ge, D Yang, J Liu, W Zhang - Energies, 2021 - mdpi.com
Short-term residential load forecasting is the precondition of the day-ahead and intra-day
scheduling strategy of the household microgrid. Existing short-term electric load forecasting …
scheduling strategy of the household microgrid. Existing short-term electric load forecasting …
Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative …
Accurate short-term forecasting of the individual residential load is a challenging task due to
the nonlinear behavior of the residential customer. Moreover, there are a large number of …
the nonlinear behavior of the residential customer. Moreover, there are a large number of …
A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior
W Yang, J Shi, S Li, Z Song, Z Zhang, Z Chen - Applied Energy, 2022 - Elsevier
With the growth of residential load and the popularity of intelligent devices, resident users
have become important target customers for demand response (DR). However, due to the …
have become important target customers for demand response (DR). However, due to 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 …
Hybrid artificial neural network system for short-term load forecasting
SA Ilić, SM Vukmirović, AM Erdeljan, FJ Kulić - Thermal Science, 2012 - doiserbia.nb.rs
This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF). The
system comprises of two Artificial Neural Networks (ANN), assembled in a hierarchical order …
system comprises of two Artificial Neural Networks (ANN), assembled in a hierarchical order …
Electrical load-temperature CNN for residential load forecasting
M Imani - Energy, 2021 - Elsevier
Residential load forecasting is a challenging problem due to complex relations among the
hourly electrical load values along the time and also nonlinear relationships among the …
hourly electrical load values along the time and also nonlinear relationships among the …
A deep bi-directional long-short term memory neural network-based methodology to enhance short-term electricity load forecasting for residential applications
Unexpected fluctuations associated with electricity load consumption patterns pose a
significant threat to the stability, efficiency, and sustainability of modernized energy systems …
significant threat to the stability, efficiency, and sustainability of modernized energy systems …
A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features
Y Wang, N Zhang, X Chen - Energies, 2021 - mdpi.com
With economic growth, the demand for power systems is increasingly large. Short-term load
forecasting (STLF) becomes an indispensable factor to enhance the application of a smart …
forecasting (STLF) becomes an indispensable factor to enhance the application of a smart …