ABSTRACT
In recent years, the use of renewable energy sources has increased considerably to meet power demand worldwide, due to the increased awareness of environmental damage caused by nonrenewable sources of energy. Solar energy is among the leading sources of renewable energy. The efficiency and power output of a solar plant equipped with photovoltaic (PV) panels depends greatly on weather conditions. Forecasting weather conditions accurately allows utility companies and system operators to calculate the amount of energy a PV plant can deliver and, in case of open-access market, to enable the dealers to acquire the best rate for every megawatt generated. Various optimization and machine learning techniques have been proposed by researchers to predict the solar PV energy output. Since the PV panels are continuously exposed to sunlight and varied atmospheric conditions, proper maintenance must be ensured for producing maximum output and efficiency. Sensors that are cost efficient, robust, and sparsely distributed are being devised by researchers. Condition-based maintenance not only guarantees the operation and lifetime of PV plants but also maximizes the output of the plant. This chapter provides an insight into various optimization techniques adopted for predicting the solar output energy and the strategies and tools for condition-based maintenance of a PV system. Types of energy forecasting along with examples of implementation of condition-based maintenance of a PV plant are presented.
