Access to reliable energy is central to improvements in living standards and is a recognized Sustainable Development Goal. This study moves beyond counting the electrified households and examines the effect of the hours of electricity households receives on their welfare. We hypothesize that additional hours of electricity have different effects on the poor, the middle income and the rich, in rural and urban areas. The methods used are panel fixed effects instrumental variables, cross sectional fixed effects instrumental variables, and logistic regression with data from the Indian Human Development Survey 2005-2012. We focus on extensive and the intensity margins, i.e. how access and additional hours of electricity affect household welfare in terms of consumption expenditure, income, assets and poverty status. The results show large gaps between benefits and costs of electricity supply among consumer groups. We also find that electricity theft is positively correlated with the net returns from electrification. A progressive pricing mechanism with targeted subsidies for the poor could therefore increase household welfare while reducing the financial losses of the State Electricity Boards.

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Gravitational water vortex power generation plant is ultra-low head micro hydro concept which requires mere 0.7-2m of height. GWVPP is based on the principle of power generation with rotation of turbine with the help of vortex generated due to basin structure when water can pass tangentially. This technology is in a primitive phase of development in various part of world. So, developers across the world are interested on how it performs in real site as only few real installations have been made far. This paper attempts to analyze the performance of different scale down model of GWVPP. First, the performance is compared among various experimental studies and pilot installations done so far in Nepal. After that the analysis of different computational studies is performed. To accesses the validation of the result obtained from the past researches, 1:20 scale down model of a plant which is to be installed in Johannesburg South Africa is developed and whose computational and experimental result is compared and predicted the model performance.

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.

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