Green energy estimation using remote sensing techniques: A comprehensive review of methods, applications, and future directions

Remote sensing (RS) techniques possess this capability and are increasingly being utilized in assessing and estimating renewable sources of energy. In this review article, recent advances in estimating solar, wind, hydro, and biomass energies using satellite, aerial, and Unmanned Arial Vehicle based RS techniques and integrating them with geographic information systems (GIS) and artificial intelligence (AI)/Machine Learning (ML) techniques are discussed. Instead of comparing error values of various domains, this review article focuses on using normalized error values to compare and assess various techniques in estimating renewable sources of energy. For estimating solar energies, satellite-based irradiance models and ML techniques typically report errors in predicting solar irradiance values as a percentage of observed irradiance values, ranging from 10 to 30 %. Similarly, wind energy estimation techniques typically report wind speeds with prediction error values ranging from 1 to 3 m/s, which corresponds to 5–12 % error in predicting wind-power output values. For estimating hydro energies, various models are typically evaluated based on hydrological efficiency values, comparing simulated river discharge values with observed values, and are typically in the range of 0.7–0.9 when well-calibrated models are considered. Similarly, in estimating biomass and bioenergies, accuracy of prediction is typically evaluated using percentage error values in predicting biomass and yield values, ranging from 10 to 25 %. In all of these techniques, accuracy values are typically variable and depend on various parameters such as geographic locations, ground truth values, and forecasting horizons. Instead of comparing individual accuracy values of various techniques in estimating renewable sources of energy, this review article attempts to provide a broader idea of accuracy values and evaluation practices in various techniques of estimating renewable sources of energy.