ABSTRACT
World transformation has meaningfully abridged the chief power sources in terms of gas, diesel, and coal. Therefore, substitute power sources based on renewable energy mainly focus on fulfilling the energy demand of the world as well as avoiding global warming. Amid different energy sources, solar energy is the key source of substitute energy used for producing electricity using photovoltaic (PV). Conversely, energy engendering performance is extremely dependent on cyclical and environmental factors. The changeable performance of environment shakes energy productivity and tends to create a disapproving influence on constancy, dependability, and the process of grid. Therefore, a precise prediction of PV productivity is critically required to guarantee constancy and dependability of grid. The detailed study to review the perilous techniques is based on PV forecast using machine learning techniques. In this chapter, different types of renewable energy with their merits and demerits are summarized. This chapter also demonstrates the key challenges existing in the current renewable energy using machine learning and various artificial intelligence applications in real-life scenarios. Finally, an inclusive analysis of machine learning techniques in renewable energy through detailed literature survey is presented for better forecasting of energy production in near future.
