Cited by Projecting Road Traffic Fatalities in Australia: Insights for Targeted Safety Interventions

Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management DOI

Waqar Waheed, Qingshan Xu

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 232, P. 110376 - 110376

Published: April 10, 2024

Language: Английский

Citations

Projecting Road Traffic Fatalities in Australia: Insights for Targeted Safety Interventions DOI

Alì Soltani,

Saeid Afshari,

Mohammad Amin Amiri

et al.

Injury, Journal Year: 2025, Volume and Issue: unknown, P. 112166 - 112166

Published: Jan. 1, 2025

Language: Английский

Citations

Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review DOI

Julius Adinkrah,

Francis Kemausuor,

Eric Tutu Tchao

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 210, P. 115161 - 115161

Published: Dec. 4, 2024

Language: Английский

Citations

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids DOI

Manoj kumar M.V,

C. Bharatiraja,

S. Devakirubakaran

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0300803 - e0300803

Published: March 21, 2024

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. integration of EVs into the grid increased demand for power while maintaining grid’s balance and efficiency. Demand Side Management (DSM) plays a pivotal role this system, ensuring that can accommodate additional load without compromising stability or necessitating costly infrastructure upgrades. In work, DSM algorithm been developed with appropriate objective functions necessary constraints, including EV load, distributed generation from Solar Photo Voltaic (PV), Battery Energy Storage Systems. are constructed using various optimization strategies, such as Bat Optimization Algorithm (BOA), African Vulture (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, Slime Mould (SMA). This algorithm-based method is simulated MATLAB/Simulink different cases loads, residential Information Technology (IT) sector loads. results show peak reduced 4.5 MW to 2.6 MW, minimum raised 0.5 1.2 successfully reducing gap between low points. Additionally, performance each was compared terms difference valley points, computation time, convergence rate achieve best fitness value.

Language: Английский

Citations

Research on optimization of power system load response strategy and cost–benefit analysis based on electricity price algorithm model DOI

Quanfeng Geng,

Kai Yang, Jing Li

et al.

International Journal of Low-Carbon Technologies, Journal Year: 2025, Volume and Issue: 20, P. 73 - 81

Published: Jan. 1, 2025

Abstract This article proposes an innovative framework that amalgamates deep reinforcement learning (DRL) with cost–benefit analysis (CBA). The enhanced actor–critic DRL algorithm simultaneously addresses short-term price fluctuations and long-term system benefits, facilitating optimization across multiple time scales. Furthermore, it establishes a dynamic, multidimensional CBA model encompasses comprehensive evaluation of economic, social, technological employing fuzzy method for quantitative analysis. integration forms closed-loop continuously refines strategy through real-time adjustments the reward function weights. Experimental results validate efficacy this approach.

Language: Английский

Citations

Short-Term Electricity-Load Forecasting by deep learning: A comprehensive survey DOI

Qi Dong, Rubing Huang, Chenhui Cui

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 154, P. 110980 - 110980

Published: May 6, 2025

Language: Английский

Citations

A Cross-Dimensional Analysis of Data-Driven Short-Term Load Forecasting Methods with Large-scale Smart Meter Data DOI

Han Li, Miguel Heleno,

Wanni Zhang

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115909 - 115909

Published: May 1, 2025

Language: Английский

Citations

Development and Testing of an IoT Platform with Smart Algorithms for Building Energy Management Systems DOI

Fayzul Islam,

Ibrahim Ahmed, Lucian Mihet‐Popa

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115970 - 115970

Published: June 1, 2025

Language: Английский

Citations

End-to-End Top-Down Load Forecasting Model for Residential Consumers DOI

Barkha Parkash,

Tek Tjing Lie, Weihua Li

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(11), P. 2550 - 2550

Published: May 24, 2024

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on principles a top-down (TD) approach. technique employs neural network predicting at lower hierarchical levels aggregated one top. A simulation is carried out with 9 (from 2013 to 2021) years energy consumption data 50 houses located in United States America. Simulation results demonstrate that E2E model, which uses single model different nodes and approach, shows huge potential improving accuracy, making it valuable tool grid planners. Model inputs are derived from category specific cluster targeted forecasting. The proposed can accurately forecast any without requiring hyperparameter adjustments. According experimental analysis, outperformed two-stage methodology benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) Support Vector Regression (SVR) by mean absolute percentage error (MAPE) 2.27%.

Language: Английский

Citations

Application of Forecasting Models in Electrical Engineering: A Systematic Literature Review DOI

Zainab Koubaa, Adnen El Amraoui, François Delmotte

et al.

Published: April 27, 2024

Language: Английский

Citations