A Novel Intelligent Scheme for Building Energy Prediction Based On Machine Learning and Deep Learning Algorithms DOI

M Jayashankara,

Prasenjit Chanak, Sanjay Kumar Singh

et al.

Published: Jan. 1, 2024

Language: Английский

Comparison of control strategies for efficient thermal energy storage to decarbonize residential buildings in cold climates: A focus on solar and biomass sources DOI

Peimaneh Shirazi,

Amirmohammad Behzadi, Pouria Ahmadi

et al.

Renewable Energy, Journal Year: 2023, Volume and Issue: 220, P. 119681 - 119681

Published: Nov. 18, 2023

Language: Английский

Citations

11
IntDEM: an intelligent deep optimized energy management system for IoT-enabled smart grid applications DOI

P. Ganesh,

B. Meenakshi Sundaram, Praveen Kumar Balachandran

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 24, 2024

Language: Английский

Citations

4
Long-term policy guidance for sustainable energy transition in Nigeria: A deep learning-based peak load forecasting with econo-environmental scenario analysis DOI
Israel A. Bayode, Abdulrahman H. Ba-Alawi, Hai-Tra Nguyen

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135707 - 135707

Published: March 1, 2025

Language: Английский

Citations

0
Enhanced electrical and thermal energy storage systems performance in smart building using FLHNN and BWOA approach DOI

B. Venkata Prasanth,

Karthikeyan Gopalsamy

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 122, P. 116651 - 116651

Published: April 22, 2025

Language: Английский

Citations

0
Review of load frequency control in modern power systems: a state-of-the-art review and future trends DOI

Samuel Sunday Yusuf,

Abdullahi Bala Kunya,

Adamu Saidu Abubakar

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 13, 2024

Language: Английский

Citations

3
Optimized LSTM-Based Electric Power Consumption Forecasting for Dynamic Electricity Pricing in Demand Response Scheme of Smart Grid DOI Creative Commons

P. Balakumar,

Senthil Kumar R

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104356 - 104356

Published: Feb. 1, 2025

Language: Английский

Citations

0
LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting DOI Open Access
Te Li, Min Zheng, Yan Zhou

et al.

Journal of Organizational and End User Computing, Journal Year: 2025, Volume and Issue: 37(1), P. 1 - 29

Published: Feb. 21, 2025

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional demand forecasting methods often struggle with complex data processing low prediction accuracy. To address these issues, this paper introduces novel approach that combines deep learning techniques decision support systems. The model integrates advanced techniques, including LSTM Transformer, PSO algorithm parameter optimization, significantly enhancing predictive performance practical applicability. Results show our achieves substantial improvements across various metrics, 30% reduction in MAE, 20% decrease MAPE, 25% drop RMSE, 35% decline MSE. These results validate model's effectiveness reliability forecasting. This research provides valuable insights applying

Language: Английский

Citations

0
CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid DOI Creative Commons

Xuhan Li

Frontiers in Energy Research, Journal Year: 2023, Volume and Issue: 11

Published: July 26, 2023

Introduction: Smart grid (SG) technologies have a wide range of applications to improve the reliability, economics, and sustainability power systems. Optimizing large-scale energy storage for smart grids is an important topic in optimization. By predicting historical load electricity price system, reasonable optimization scheme can be proposed. Methods: Based on this, this paper proposes prediction model combining convolutional neural network (CNN) gated recurrent unit (GRU) based attention mechanism explore grid. The CNN extract spatial features, GRU effectively solve gradient explosion problem long-term forecasting. Its structure simpler faster than LSTM models with similar accuracy. After CNN-GRU extracts data, features are finally weighted by module performance further. Then, we also compared different forecasting models. Results Discussion: results show that our has better predictive computational power, making contribution developing schemes grids.

Language: Английский

Citations

9
Solving electric power distribution uncertainty using deep learning and incentive-based demand response DOI

P. Balakumar,

Vinopraba Thirumavalavan,

Geetha Chandrasekaran

et al.

Utilities Policy, Journal Year: 2023, Volume and Issue: 82, P. 101579 - 101579

Published: May 12, 2023

Language: Английский

Citations

6
Review of Artificial Neural Network Approaches for Predicting Building Energy Consumption DOI

Siti Solehah Md Ramli,

Mohammad Nizam Ibrahim,

A. Mohamad

et al.

Published: March 6, 2023

Recently, the forecasting of energy consumption has prompted a massive escalation in research studies that are being conducted all over world an effort to attain higher levels sustainability. Forecasting is essential decision-making for effective conservation and development within organization. The adoption data-driven models seen tremendous growth past few decades as result improvements performance, robustness, simplicity deployment brought about by these improvements. There various kinds models, but Artificial Neural Networks (ANN) currently among most widely used methods have been applied real-world situations. This study provides comprehensive overview on ANN comparison with other evaluation metrics were employed evaluate performances each technique. review helps outline potential future area building prediction prominence existing gaps.

Language: Английский

Citations

4