A novel hybrid method for solar power prediction

Renewable energy sources, particularly solar energy, play a vital role for generating environment-friendly electricity. Foremost advantages of solar energy sources are: nonpolluting, free in terms of availability and renewable. The renewable green-energy sources are becoming more cost-effective and sustainable substitutes to conventional fossil fuels. Nonetheless, power generation from Photovoltaic (PV) systems is unpredictable due to its reliance on meteorological conditions. The effective use of this fluctuating solar energy source obliges reliable and robust forecast information for management and operation of a contemporary power grid. Due to the remarkable proliferation of solar power generation, the prediction of solar power yields becomes more and more imperative. Large-scale penetration of solar power in the electricity grid provides numerous challenges to the grid operator, mainly due to the intermittency of the sun. Since the power produced by a PV depends decisively on the unpredictability of the sun, unexpected variations of a PV output may increase operating costs for the electricity system as well as set potential threats to the reliability of electricity supply. Nevertheless, the prediction accuracy level of the existing prediction methods for solar power is not up to the mark that is very much required to deal with the forthcoming sophisticated and advanced power grid like Smart Grid. Therefore, accurate solar power prediction methods become very substantial. The main goal of this thesis is to produce a novel hybrid prediction method for more accurate, reliable and robust solar power prediction using modern Computational Intelligence (CI). The hybrid prediction method which is mainly composed of multiple regressive machine learning techniques will be as accurate and reliable as possible, to accommodate the needs of any future systems that depend upon it for generator or load scheduling, or grid stability control applications. In this thesis, research on the experimental analysis and development of hybrid machine learning for solar power prediction has been presented. The thesis makes the following major contributions: 1) It investigates heterogeneous machine learning techniques for hybrid prediction methods for solar power 2) It applies feature selection methods to individually improve the prediction accuracy of previous machine learning techniques 3) It investigates possible parameter optimisation of computational intelligence techniques to make sure that individual predictions are as accurate as possible 4) It proposes hybrid prediction by non-linearly integrating the discrete prediction results from various machine-learning techniques. Performance characteristics of the hybrid machine learning over individuals was carried out through experimental analysis and the results are justified by various statistical tests and error validation metrics which confirmed the maximum achievable accuracy of the developed hybrid method for solar power prediction. It is expected that the outcome of the research will provide noteworthy contribution to the relevant research field as well as to Australian power industries in the near future.