Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions
This research, conducted throughout the years 2022 and 2023, examines the role of
blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It …
blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It …
Energy Forecasting: A Comprehensive Review of Techniques and Technologies
Distribution System Operators (DSOs) and Aggregators benefit from novel energy
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …
Forecasting solar power generation utilizing machine learning models in Lubbock
Solar energy is a widely accessible, clean, and sustainable energy source. Solar power
harvesting in order to generate electricity on smart grids is essential in light of the present …
harvesting in order to generate electricity on smart grids is essential in light of the present …
Energy load forecasting: One-step ahead hybrid model utilizing ensembling
In the light of the adverse effects of climate change, data analysis and Machine Learning
(ML) techniques can provide accurate forecasts, which enable efficient scheduling and …
(ML) techniques can provide accurate forecasts, which enable efficient scheduling and …
[HTML][HTML] Advances in the Design of Renewable Energy Power Supply for Rural Health Clinics, Case Studies, and Future Directions
Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of
any society. Unfortunately, several distant communities far from the national grid do not have …
any society. Unfortunately, several distant communities far from the national grid do not have …
Urban traffic congestion prediction: a multi-step approach utilizing sensor data and weather information
N Tsalikidis, A Mystakidis, P Koukaras, M Ivaškevičius… - Smart Cities, 2024 - mdpi.com
The continuous growth of urban populations has led to the persistent problem of traffic
congestion, which imposes adverse effects on quality of life, such as commute times, road …
congestion, which imposes adverse effects on quality of life, such as commute times, road …
Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models
The building sector, known for its high energy consumption, needs to reduce its energy use
due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy …
due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy …
Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods
Y Liu, MS Saleem, J Rashid, S Ahmad… - Energy Science & …, 2024 - Wiley Online Library
Accurate forecasts of renewable and nonrenewable energy output are essential for meeting
global energy needs and resolving environmental issues. Energy sources like the sun and …
global energy needs and resolving environmental issues. Energy sources like the sun and …
Power Load Forecasting: A Time-Series Multi-Step Ahead and Multi-Model Analysis
A Mystakidis, N Tsalikidis, P Koukaras… - 2023 58th …, 2023 - ieeexplore.ieee.org
Distribution System Operators and Aggregators can derive benefits from innovative
approaches in Power or Energy Load Forecasting (PLF-ELF). Enhanced accuracy in PLF …
approaches in Power or Energy Load Forecasting (PLF-ELF). Enhanced accuracy in PLF …
Recent Advances and Future Challenges of Solar Power Generation Forecasting
The unprecedented growth of Renewable Energy Sources (RES) positions solar power as a
leading contender in the global energy mix. Solar energy offers a sustainable alternative to …
leading contender in the global energy mix. Solar energy offers a sustainable alternative to …