|Fig. 1: Photovoltaic panels in Southern California. (Source: Wikimedia Commons).|
Photovoltaic systems make use of photovoltaic cells to convert light from the sun into energy. These cells are typically made of silicon, or another semiconductor material, and one side of the cell is positively charged while the other is negatively charged. They produce electricity when sunlight strikes the positive side, exciting the negative side's electrons and creating a current. Photovoltaic cells are configured into arrays and modules, as seen in the photo of solar panels in Fig. 1. When sunlight hits these cells, direct current (DC) electricity is generated, having the ability to operate motors, charge batteries, and power other objects. Additionally, photovoltaic systems can also power appliances and work in conjunction with the utility grid by producing alternating current electricity.
A major concern surrounding solar power is the variability and unpredictability of sunlight. If it is overcast or cloud cover present during the day, as seen in Fig. 2, then the photovoltaic cells are unable to produce electricity, or will do so inefficiently. This inherent variability poses issues with grid reliability and the expenses associated with operating the solar units. Moreover, peak electricity demand usually occurs when it is dark outside, when solar production is zero.  Additionally, the most idea places for solar panels are typically far away from consumers, and transferring the energy to them is very expensive. All of these factors make it difficult to predict the photovoltaic output of solar panels, and photovoltaic forecasting is a method used to address this issue. 
|Fig. 2: The unpredictability of sunlight poses a challenge to energy facilities and solar power operators. (Source: Wikimedia Commons)|
With the large increase in the number of photovoltaic systems, the solar industry has placed a recent importance on solar forecasting. Large capacity photovoltaic power generation was installed in Germany (38.24 GW), China (28.05 GW), Italy (18.31 GW), Japan (23.3 GW), U.S.A. (18.28 GW), and Spain (5.39 GW) by the end of 2014.  Since solar generation is variable with the unpredictability of sunlight in different places on Earth, the ability to predict solar output will provide the solar energy industry with a great advantage. Cloud cover impedes sunlight from hitting solar panels. However, by being able to predict when and where sunlight will strike, photovoltaic panels will greatly increase efficiency.  Forecasting systems can help to regulate the photovoltaic systems and to determine the dispatching of the energy created.
Discovering methods and technology for accurate and reliable solar forecasting will be imperative in the future of solar energy. With a lack of solar forecasting and underdeveloped technology surrounding it, inaccurate forecasts can have expensive consequences. Grid operators must make up for these inaccurate forecasts by using short-term sources of power when not enough is generated, and this process is costly. Solar forecasting is a relatively new technology, but steps are being taken to determine methods to help predict the occurrence of sunlight. For instance, UC San Diego is has built a smart microgrid on its campus with a single sky imager that predicts the movement of clouds to assist in operating the solar panels. The next step will be to develop a reliable technology that can accurately predict sunlight in specific areas to balance the supply and demand of solar grids.
© Madie Chou. The author warrants that the work is the author's own and that Stanford University provided no input other than typesetting and referencing guidelines. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.
 I. M. Burnett, "Energy Storage and the California 'Duck Curve'," PH240, Stanford University, Fall 2015.
 S. Pelland et al., "Photovoltaic and Solar Forecasting: State of the Art," International Energy Agency, Report IEA PVPS T14-10:2013, October 2013.
 C. Wan et al., "Photovoltaic and Solar Power Forecasting for Smart Grid Energy Management," CSEE J. Power Energy Syst. 1, 38 (2015).