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The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world's energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model was defined and trained, the model performance was assessed qualitative (by graphical tools) and quantitative (by calculating the Root Mean Square Error (RMSE) and by applying the k-fold cross-validation method). The results showed that our model can predict well, since the RMSE gives a value of 0.11368, whereas when applying the k-fold cross-validation, the mean of the resulting RMSE values is 0.09394 with a standard deviation 0.01616. [ABSTRACT FROM AUTHOR]

China and India are not only the two most populous nations on Earth, they are also two of the most rapidly growing economies. Historically, economic and social development have been subsidized by cheap and abundant fossil-fuels. Climate change from fossil-fuel emissions has resulted in the need to reduce fossil-fuel emissions in order to avoid catastrophic warming. If climate goals are achieved, China and India will have been the first major economies to develop via renewable energy sources. In this article, we examine the factors of projected population growth, available fossil-fuel reserves, and renewable energy installations required to develop scenarios in which both China and India may increase per capita energy consumption while remaining on trach to meet ambitious climate goals. Here, we show that China and India will have to expand their renewable energy infrastructure at unprecedented rates in order to support both population growth and development goals. In the larger scope of the literature, we recommend community-based approaches to microgrid and cookstove development in both China and India. [ABSTRACT FROM AUTHOR]