The role of utilizing artificial intelligence and renewable energy in reaching sustainable development goals

FM Talaat, AE Kabeel, WM Shaban - Renewable Energy, 2024 - Elsevier
Many nations want to use only renewable energy by 2050. Given the recent rapid expansion
in RE use in the global energy mix and its progressive impact on the global energy sector …

[HTML][HTML] Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous Federated smart grids

HU Manzoor, A Jafri, A Zoha - Internet of Things, 2024 - Elsevier
Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

[HTML][HTML] Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications

SM Sharifhosseini, T Niknam, MH Taabodi… - Energies, 2024 - mdpi.com
Electrical power systems are the lifeblood of modern civilization, providing the essential
energy infrastructure that powers our homes, industries, and technologies. As our world …

Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary …

MB Gorzałczany, F Rudziński - Energies, 2024 - mdpi.com
This paper addresses the problem of accurate and interpretable prediction of energy
consumption in residential buildings. The solution that we propose in this work employs the …

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

H Min, S Hong, J Song, B Son, B Noh, J Moon - Electronics, 2024 - mdpi.com
We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize
photovoltaic (PV) power forecasting in South Korea. This model uses a self-attention-based …

[PDF][PDF] Lightweight single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous smart grids

HU Manzoor, A Jafri, A Zoha - Authorea Preprints, 2024 - researchgate.net
Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging
data from distributed load networks while preserving data privacy. However, the …

[HTML][HTML] Modeling the Efficiency of Resource Consumption Management in Construction Under Sustainability Policy: Enriching the DSEM-ARIMA Model

P Sutthichaimethee, G Mentel, V Voloshyn, H Mishchuk… - Sustainability, 2024 - mdpi.com
The aim of this research is to study the influence of factors affecting the efficiency of resource
consumption under the sustainability policy based on using the DSEM-ARIMA (Dyadic …

Optimizing deep neural network architectures for renewable energy forecasting

S khan, T Mazhar, T Shahzad, W Waheed… - Discover …, 2024 - Springer
An accurate renewable energy output forecast is essential for energy efficiency and power
system stability. Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated …

[HTML][HTML] A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting

A Vakhnin, I Ryzhikov, H Niska, M Kolehmainen - AI, 2024 - mdpi.com
Accurately forecasting power consumption is crucial important for efficient energy
management. Machine learning (ML) models are often employed for this purpose. However …

[HTML][HTML] Bus Basis Model Applied to the Chilean Power System: A Detailed Look at Chilean Electric Demand

C Benavides, S Gwinner, A Ulloa, J Barrales-Ruiz… - Energies, 2024 - mdpi.com
This paper presents a methodology to forecast electrical demand for the Chilean Electrical
Power System considering a national, regional, district and bus spatial disaggregation. The …