Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting
Temperature forecasting is a crucial aspect of meteorology and climate change studies, but
challenges arise due to the complexity of time series data involving seasonal patterns and …
challenges arise due to the complexity of time series data involving seasonal patterns and …
A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies
V Simankov, P Buchatskiy, A Kazak, S Teploukhov… - Energies, 2024 - mdpi.com
The use of renewable energy sources is becoming increasingly widespread around the
world due to various factors, the most relevant of which is the high environmental …
world due to various factors, the most relevant of which is the high environmental …
Exploring the landscape of deep learning for solar photovoltaic power output forecasting: A review
The rise of distributed energy resources stems from reliance on carbon-intensive energy and
climate concerns. While photovoltaic solar energy leads in modern grids, its intermittent …
climate concerns. While photovoltaic solar energy leads in modern grids, its intermittent …
Ltpnet integration of deep learning and environmental decision support systems for renewable energy demand forecasting
T Li, M Zhang, Y Zhou - arxiv preprint arxiv:2410.15286, 2024 - arxiv.org
Against the backdrop of increasingly severe global environmental changes, accurately
predicting and meeting renewable energy demands has become a key challenge for …
predicting and meeting renewable energy demands has become a key challenge for …
Optimizing biomass energy production in the southern region of Iran: A deterministic MCDM and machine learning approach in GIS
M Mokarram, SRA Ronizi, S Negahban - Energy Policy, 2024 - Elsevier
This study employs a deterministic approach, distinguishing itself from other renewable
energy evaluations, to assess the potential of electrical energy derived from biomass …
energy evaluations, to assess the potential of electrical energy derived from biomass …
Solar photovoltaic panel production in Mexico: A novel machine learning approach
This study examines the potential for widespread solar photovoltaic panel production in
Mexico and emphasizes the country's unique qualities that position it as a strong …
Mexico and emphasizes the country's unique qualities that position it as a strong …
A new Takagi–Sugeno–Kang model to time series forecasting
A fuzzy inference system consists of a machine learning concept that combines accuracy
and interpretability. They are divided into two main groups: Mamdani and Takagi–Sugeno …
and interpretability. They are divided into two main groups: Mamdani and Takagi–Sugeno …
[HTML][HTML] An Intelligent SARIMAX-Based Machine Learning Framework for Long-Term Solar Irradiance Forecasting at Muscat, Oman
The intermittent nature of renewable energy sources (RES) restricts their widespread
applications and reliability. Nevertheless, with advancements in the field of artificial …
applications and reliability. Nevertheless, with advancements in the field of artificial …
Solar energy prediction in IoT system based optimized complex-valued spatio-temporal graph convolutional neural network
The accurate prediction of solar energy generation is significant for efficient energy
management in Internet of Things (IoT) devices. However, current forecasting models …
management in Internet of Things (IoT) devices. However, current forecasting models …
[HTML][HTML] Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture
Since the advent of smart agriculture, technological advancements in solar energy have
significantly improved farming practices, resulting in a substantial revival of different crop …
significantly improved farming practices, resulting in a substantial revival of different crop …