Abstract
Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.