| 參考文獻: | 李鴻洲，2018。台電月刊666期。臺北：台灣電力股份有限公司。
戴寶通、鄭晃忠，1998。太陽能電池技術手冊。新竹：台灣電子材料與元件協會
饒瑞琦(2011)。太陽光電發電系統效能與可用度之研究，清雲科技大學電機工程系所學位論文 。
Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry, 11(2), 240.
Europe, S. (2018). Global Market Outlook for Solar Power 2018–2022. Solar Power Europe: Brussels, Belgium.
Ghofrani, M., & Alolayan, M. (2017). Time Series and Renewable Energy Forecasting. In Time Series Analysis and Applications: IntechOpen.
Jahanshahi, A., Jahanianfard, D., Mostafaie, A., & Kamali, M. (2019). An Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of energy consumption by household sector in Euro area.
Lowder, T., Mendelsohn, M., Speer, B., & Hill, R. (2013). Continuing developments in PV risk management: strategies, solutions, and implications. Retrieved from
Mapfumo, S., Groenendaal, H., & Dugger, C. (2017). Risk Modeling for Appraising Named Peril Index Insurance Products: A Guide for Practitioners: The World Bank.
Sawin, J. L., Rutovitz, J., Sverrisson, F., Aberg, E., Adib, R., Appavou, F., . . . Wuester, H. (2018). Renewables 2018. Global status report 2018 |
