Solar Power Forecasting

Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately predict solar power. We first explored the potential of instance-based methods and proposed two new methods: the data source weighted nearest neighbour (DWkNN) and the extended Pattern Sequence Forecasting (PSF) algorithms. DWkNN uses multiple data sources and considers their importance by learning the best weights based on previous data. PSF1 and PSF2 extended the standard PSF algorithm deal with data from multiple related time series. Then, we proposed two clustering-based methods for PV power prediction: direct and pair patterns. We used clustering to partition the days into groups with similar weather characteristics and then created a separate PV power prediction model for each group. The direct clustering groups the days based on their weather profiles, while the pair patterns consider the weather type transition between two consecutive days. We also investigated ensemble methods and proposed static and dynamic ensembles of neural networks. We first proposed three strategies for creating static ensembles based on random example and feature sampling, as well as four strategies for creating dynamic ensembles by adaptively updating the weights of the ensemble members based on past performance. We then explored the use of meta-learning to further improve the performance of the dynamic ensembles. The methods proposed in this thesis can be used by PV plant and electricity market operators for decision making, improving the utilisation of the generated PV power, planning maintenance and also facilitating the large-scale integration of PV power in the electricity grid

Predicting the Amplitude of a Solar Cycle Using the North-South Asymmetry in the Previous Cycle: II. An Improved Prediction for Solar Cycle~24

Recently, using Greenwich and Solar Optical Observing Network sunspot group data during the period 1874-2006, (Javaraiah, MNRAS, 377, L34, 2007: Paper I), has found that: (1) the sum of the areas of the sunspot groups in 0-10 deg latitude interval of the Sun's northern hemisphere and in the time-interval of -1.35 year to +2.15 year from the time of the preceding minimum of a solar cycle n correlates well (corr. coeff. r=0.947) with the amplitude (maximum of the smoothed monthly sunspot number) of the next cycle n+1. (2) The sum of the areas of the spot groups in 0-10 deg latitude interval of the southern hemisphere and in the time-interval of 1.0 year to 1.75 year just after the time of the maximum of the cycle n correlates very well (r=0.966) with the amplitude of cycle n+1. Using these relations, (1) and (2), the values 112 + or - 13 and 74 + or -10, respectively, were predicted in Paper I for the amplitude of the upcoming cycle 24. Here we found that in case of (1), the north-south asymmetry in the area sum of a cycle n also has a relationship, say (3), with the amplitude of cycle n+1, which is similar to (1) but more statistically significant (r=0.968) like (2). By using (3) it is possible to predict the amplitude of a cycle with a better accuracy by about 13 years in advance, and we get 103 + or -10 for the amplitude of the upcoming cycle 24. However, we found a similar but a more statistically significant (r=0.983) relationship, say (4), by using the sum of the area sum used in (2) and the north-south difference used in (3). By using (4) it is possible to predict the amplitude of a cycle by about 9 years in advance with a high accuracy and we get 87 + or - 7 for the amplitude of cycle 24.Comment: 21 pages, 7 figures, Published in Solar Physics 252, 419-439 (2008

CERES: Clouds and the Earth's Radiant Energy System

This brochure gives a brief description of the science research that is being done with data from the Clouds and Earth's Radiant Energy System (CERES) instrument flying onboard NASA's Terra satellite. It also contains information about some of the data products and technical specifications. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional