As the exhaust rate of the conventional sources has geared up already, this is compelling the power industries to install the power plants based on the non-conventional sources so that future demand of the energy supply can be fulfilled. Among the various sources of renewable energy like wind, hydro, tidal etc., solar energy is the most easily accessible and available renewable energy source. Ensuring the feasibility of any energy source not only technical but also the economical perspective is the most important criteria. This paper has incorporated both the perspective and has done the techno-economic analysis to determine the optimum combination of the PV array size and battery size to minimize the overall electricity generation per unit. In this paper, a standalone solar PV system has been analyzed for the location of Jamshedpur, where an effort has been done to choose the optimum combination of the solar array and battery size within the desired range of LLP so that the electricity generation cost per unit can be minimized. The overall duration of the analysis has been done for a year and the outcome of the research has been verified with the help of MATLAB software.
Summary
Spatiotemporal solar radiation forecasting is extremely challenging due to its dependence on metrological and environmental factors. Chaotic time‐varying and non‐linearity make the forecasting model more complex. To cater this crucial issue, the paper provides a comprehensive investigation of the deep learning framework for the prediction of the two components of solar irradiation, that is, Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI). Through exploratory data analysis the three recent most prominent deep learning (DL) architecture have been developed and compared with the other classical machine learning (ML) models in terms of the statistical performance accuracy. In our study, DL architecture includes convolutional neural network (CNN) and recurrent neural network (RNN) whereas classical ML models include Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB), and K‐Nearest Neighbor (KNN). Additionally, three optimization techniques Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO) have been incorporated for tuning the hyper parameters of the classical ML models to obtain the best results. Based on the rigorous comparative analysis it was found that the CNN model has outperformed all classical machine learning and DL models having lowest mean squared error and highest R‐Squared value with least computational time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.