Short-term solar forecasting is essential for the large-scale application of solar energy and is necessary for the operation of power plants, energy trading, and grid balancing. The main cause of uncertainty in solar forecasting is cloud movement, which can be observed by All-Sky Images. The spatial layout and temporal dynamics of clouds cannot be extracted by conventional cloud modelling approaches utilising image analysis techniques, leading to inaccurate predictions of the interaction with solar radiation. In this study an categorization method is made based on sky conditions and from that five classes are made. The goal of this classification method is to improve the 21-minute irradiance predictions which are made with a deep learning model. The outcome of the 21-minute deep learning model will get compared with the Persistence, Smart Persistence and ARIMA model. This classification method and irradiance prediction are applied for location Folsom, California and Delft, Netherlands. The irradiance predictions based on sky classification showed an improvement of 8.6% for location Delft and 29.3% for location Folsom when the same dataset sizes are used. The irradiance predictions were also done for various dataset sizes and compared per location. Finally, it provides a way forward to improve the classification method and deep learning models to anticipate short-term irradiance in the future.

Fight against climate change is facilitated through energy transition which entails changing the fossil fuel based energy generation to sustainable resources such as solar PV. However, PV installations require extensive resources for their development and construction, so it is essential that the efficiency of energy conversion is high. A very important factor to ensure PV system efficiency is the use of maximum power point tracking (MPPT). MPPT controls the system's operating point so that at each instance power drawn from a PV module or array is the maximum power available. This is realised by employing a DC-DC converter controlled by an MPPT algorithm. The most widely used algorithm due to its simplicity and cost of implementation is the Perturb and Observe (P&O) algorithm. However, its performance is not ideal in both steady-state and dynamic conditions. The algorithm's operation depends on the P&O parameters and conditions in which the algorithm operates are influenced by irradiance variability.
There are several studies on P&O algorithm efficiency and irradiance variability separately, however, a link between these two topics has not been explored. Thus, this thesis aims to bridge the gap and its purpose is to determine the relationship between P&O algorithm efficiency and irradiance variability. This was carried out by use of real 3-second irradiance data from Oahu, Hawaii, modelling the power output of a PV module and obtaining the operating point of the system by implementing a P&O algorithm without assuming any physical properties of a DC-DC converter. Then using operating point and maximum power point power values, 3 s averages of the P&O algorithm efficiency were computed, and variability metrics were calculated using the original irradiance data. Finally, an exploratory analysis of the prepared dataset was performed to determine how the P&O algorithm varies when exposed to different irradiance variability. Additionally, a sensitivity analysis was deemed necessary to examine efficiency dependence on P&O algorithm parameters - sampling interval and perturbation amplitude - in different cases of irradiance variability.

The results of this research have shown variations of P&O efficiency values on different time scales as well as in different variability conditions. Each day of the year was classified by occurring irradiance variability. It was confirmed that efficiency depends on the variability class of the day with the lowest efficiencies down to 99.8 % found for highly variable days. Monthly P&O efficiency was determined to be affected by the number of days from each class. P&O efficiency was highest when the least days in the month were classified as highly variable. Monthly efficiencies were found to be above 99.9 %. To describe the relationship between variability and P&O efficiency, variability metrics were selected and computed for 1 min periods. Major scattering of data was found when 1 min averaged efficiency was plotted against any of the variability metrics. Magnitudes of 1 min average efficiency were determined to be mostly above 95 %. To reduce scattering, average efficiencies that could be expected in bins of variability metrics were determined, and in such a case, a general decrease in efficiency was observed with increasing magnitudes of variability metrics. Additionally, polynomial fits through the data were produced to provide functions with which P&O efficiency could be approximated when variability described by a metric is known. Lastly, P&O efficiency sensitivity to its parameters study has shown that in most cases efficiency is more sensitive to perturbation amplitude, however, in high variability, sampling interval and perturbation amplitude must be sufficiently small (ΔV≤1% of V_oc(STC), T_a≤10 ms) for better performance of the algorithm.