Results 11 to 20 of about 215,326 (298)
Short-Term Load Forecasting With Deep Residual Networks [PDF]
IEEE Transactions on Smart Grid, 2019 We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks.
Kunjin Chen +5 more
openaire +4 more sources
An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting†. [PDF]
Sensors (Basel), 2021 Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time.
Jung S, Moon J, Park S, Hwang E.
europepmc +2 more sources
Deep learning-driven hybrid model for short-term load forecasting and smart grid information management. [PDF]
Sci RepAccurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic
Wen X, Liao J, Niu Q, Shen N, Bao Y.
europepmc +2 more sources
Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead
Energies, 2023 Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation.
Saima Akhtar +7 more
semanticscholar +1 more source
A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network
IEEE Access, 2021 In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation.
S. Rafi +3 more
semanticscholar +1 more source
IEEE Access, 2021 Since electricity plays a crucial role in countries’ industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling.
Behnam Farsi +3 more
semanticscholar +1 more source
A Comprehensive Study of Random Forest for Short-Term Load Forecasting
Energies, 2022 Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize.
Grzegorz Dudek
semanticscholar +1 more source
Machine Learning for Short-Term Load Forecasting in Smart Grids
Energies, 2022 A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation.
Bibi Ibrahim +3 more
semanticscholar +1 more source
IEEE Access, 2023 Short-term load forecasting is mainly utilized in control centers to explore the changing patterns of consumer loads and predict the load value at a certain time in the future. It is one of the key technologies for the smart grid implementation. The load
Xinfang Chen +4 more
semanticscholar +1 more source
Energies, 2023 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 customers and utilities for energy visualization.
Ping Ma +4 more
semanticscholar +1 more source