The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.

The study incorporates an overview of the green hydrogen-production potential from wind energy in the USA, its application in power generation and the scope of substituting grey and blue hydrogen for industrial usage. Over 10 million metric tons of grey and blue hydrogen is produced in the USA annually to fulfil the industrial demand, whereas, for 1 million metric tons of hydrogen generated, 13 million metric tons of CO₂ are released into the atmosphere. The research aims to provide a state-of-the-art review of the green hydrogen technology value chain and a case study on the production of green hydrogen from an 8-MW wind turbine installed in the southern plain region of Texas. This research estimates that the wind-farm capacity of 130 gigawatt-hours is required to substitute grey and blue hydrogen for fulfilling the current US annual industrial hydrogen demand of 10 million metric tons. The study investigates hydrogen-storage methods and the scope of green hydrogen-based storage facilities for energy produced from a wind turbine. This research focuses on the USA’s potential to meet all its industrial and other hydrogen application requirements through green hydrogen.

The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.

Renewable energy could mitigate remote area energy crises through rural electrification. Karnali province, one of the seven federal provinces of Nepal, is such a remote location and is most deprived in terms of electricity access. Around 67% of the population of the Karnali province is not connected to the national grid electricity supply. High altitude, mountainous topography makes it difficult to provide grid access to the region. This study summarizes the current electricity access status in Nepal and Karnali province specifically. The paper discusses the energy, economic and environmental (3E) analysis of different renewable energy resources like solar and wind energy for the grid-isolated region in Mugu and Jumla district of Karnali province. The study investigates the feasibility of a 200-kW solar power plant installation in Gamghadi, the capital of Mugu district and a 100-kW wind power plant installation in Tila village, Jumla district. The study suggests whether a similar installation of the distributed energy plant is a solution to mitigate the energy crisis problem in the high Himalayas regions, like Karnali province of Nepal. Based on the high-level resource assessment, the study estimates an investment cost ranging from 7 to 9 million USD would be necessary for the installation of such distributed solar PV and wind turbines.