Special Issue on Applications of Artificial Neural Networks for Energy Systems
Source abstract
Abstract
In the 21st century, newer and cleaner technologies like renewable energy technologies, fuel cells, and smart grids are promising to transform the way we generate and distribute electric energy [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Energy Load and Power Forecasting · Solar Radiation and Photovoltaics · Power Systems and Renewable Energy
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical, Electronics and Information Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
References (13)
Show source-provided references
1. Applications of artificial neural-networks for energy systems10.1016/S0306-2619(00)00005-2 ↗
2. Yin, L., Zhao, L., Yu, T., and Zhang, X. (2018). Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems. Appl. Sci., 8.10.3390/app8112185 ↗
3. Cenek, M., Haro, R., Sayers, B., and Peng, J. (2018). Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks. Appl. Sci., 8.10.3390/app8050749 ↗
4. Notton, G., Voyant, C., Fouilloy, A., Duchaud, J., and Nivet, M. (2019). Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications. Appl. Sci., 9.10.3390/app9010209 ↗
5. Wang, Y., Zou, H., Chen, X., Zhang, F., and Chen, J. (2018). Adaptive Solar Power Forecasting based on Machine Learning Methods. Appl. Sci., 8.10.3390/app8112224 ↗
6. Kim, T., Ko, W., and Kim, J. (2019). Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting. Appl. Sci., 9.10.3390/app9010204 ↗
7. Qin, Q., Lai, X., and Zou, J. (2019). Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy. Appl. Sci., 9.10.3390/app9010126 ↗
8. Genikomsakis, K., Lopez, S., Dallas, P., and Ioakimidis, C. (2017). Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting. Appl. Sci., 7.10.3390/app7111142 ↗
9. Deberneh, H., and Kim, I. (2018). Predicting Output Power for Nearshore Wave Energy Harvesting. Appl. Sci., 8.10.3390/app8040566 ↗
10. Baldinelli, A., Barelli, L., Bidini, G., Bonucci, F., and Iskenderoğlu, F. (2019). Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application. Appl. Sci., 9.10.3390/app9010051 ↗
11. Wang, C., Lu, N., Wang, S., Cheng, Y., and Jiang, B. (2018). Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery. Appl. Sci., 8.10.3390/app8112078 ↗
12. Elmeligy, A., Mehrani, P., and Thibault, J. (2018). Artificial Neural Networks as Metamodels for the Multiobjective Optimization of Biobutanol Production. Appl. Sci., 8.10.3390/app8060961 ↗
13. Wang, D., Sun, Q., Li, Y., and Liu, X. (2019). Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method. Appl. Sci., 9.10.3390/app9030520 ↗