Photovoltaic Power Prediction Model Based on Parallel Neural Network and Genetic Algorithms
Author:
Xu Gaowei,Liu Min
Publisher
Springer Singapore
Reference13 articles.
1. Sahu, B.K.: A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries. Renew. Sustain. Energy Rev. 43, 621–634 (2015) 2. Yang, H.T., Huang, C.M., Huang, Y.C., et al.: A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output. IEEE Trans. Sustain. Energy 5(3), 917–926 (2014) 3. Almonacid, F., Pérez-Higueras, P.J., Fernández, E.F., et al.: A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Convers. Manag. 85(9), 389–398 (2014) 4. Liu, J., Fang, W., Zhang, X., et al.: An improved photovoltaic power forecasting model with the assistance of aerosol index data. IEEE Trans. Sustain. Energy 6(2), 1–9 (2015) 5. Teo, T.T., Logenthiran, T., Woo, W.L.: Forecasting of photovoltaic power using extreme learning machine. In: 2015 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA), pp. 1–6. IEEE (2015)
|
|