Hybrid deep learning models for time series forecasting of solar power

D Salman, C Direkoglu, M Kusaf… - Neural Computing and …, 2024 - Springer
Forecasting solar power production accurately is critical for effectively planning and
managing renewable energy systems. This paper introduces and investigates novel hybrid …

Optimal deep learning control for modernized microgrids

SR Yan, W Guo, A Mohammadzadeh… - Applied Intelligence, 2023 - Springer
In this study, a new control approach is introduced for active/reactive power control in
modernized microgrids (MMGs). The dynamics of MMG are considered to be unknown and a …

Hybrid power generation forecasting using CNN based BILSTM method for renewable energy systems

T Anu Shalini, B Sri Revathi - Automatika: časopis za automatiku …, 2023 - hrcak.srce.hr
Sažetak This paper presents the design of a grid-connected hybrid system using modified Z
source converter, bidirectional converter and battery storage system. The input sources for …

A Multi-Factor Selection and Fusion Method through the CNN-LSTM Network for Dynamic Price Forecasting

Y Liu, C Yang, K Huang, W Liu - Mathematics, 2023 - mdpi.com
Commodity prices are important factors for investment management and policy-making, and
price forecasting can help in making better business decisions. Due to the complex and …

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

M Salcedo-Gonzalez, J Suarez-Paez, M Esteve… - … International Journal of …, 2023 - mdpi.com
This article presents the development of a geo-visualization tool, which provides police
officers or any other type of law enforcement officer with the ability to conduct the …

Efficiency of the evolutionary methods on the optimal design of secant pile retaining systems in a deep excavation

F Taiyari, M Hajihassani, M Kharghani - Neural Computing and …, 2022 - Springer
Deep large excavations in urban areas are an important engineering challenge, whereas
secant piling techniques are among the best solutions to have a safe workplace …

Adaptive Encoder-Decoder Model Considering Spatio-Temporal Features for Short-Term Power Prediction of Distributed Photovoltaic Station

X Dou, Y Deng, S Wang, T Chu, J Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Considering the impact of operation and maintenance costs and technology, there is
generally a lack of sufficient meteorological observation devices within the distributed …

Role of machine learning algorithms for wind power generation prediction in renewable energy management

T Anushalini, B Sri Revathi - IETE Journal of Research, 2023 - Taylor & Francis
The electrical energy demand is growing every day. Fossil fuel-based electrical power
generation pollutes the environment. So, to fulfil the electrical energy demand, clean …

Linguistic-Based MCDM Approach for Climate Change Risk Evaluation Methodology

G Büyüközkan, D Uztürk, Y Karabulut - … Making Using AI in Energy and …, 2023 - Springer
Conventional production networks that focus on optimizing budgets and increasing
productivity often give way to vulnerabilities, which are under threat by external shocks that …

Power generation forecasting using deep learning CNN-based BILSTM technique for renewable energy systems

T Anu Shalini, B Sri Revathi - Journal of Intelligent & Fuzzy …, 2022 - content.iospress.com
This paper presents the design of a grid connected hybrid system using modified Z source
converter, bidirectional converter and battery storage system. The input sources for the …