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Solar panel efficiency is significantly influenced by its operating temperature. Recent advancements in emerging renewable energy alternatives have enabled photovoltaic (PV) module installation over water bodies, leveraging their increased efficiency and associated benefits. This paper examines the operational performance of solar panels placed over water bodies, comparing them to ground-mounted solar PV installations. Regression models for panel temperature are developed based on experimental setups at BITS Pilani, India. Developed regression models, including linear, quadratic, and exponential, are utilized to predict the operating temperature of solar PV installations above water bodies. These models incorporated parameters such as ambient temperature, solar insolation, wind velocity, water temperature, and humidity. Among these, the one-degree regression models with three parameters outperformed the models with four or five parameters with a prediction error of 5.5 °C. Notably, the study found that the annual energy output estimates from the best model had an error margin of less than 0.2% compared to recorded data. Research indicates that solar PV panels over water bodies produce approximately 2.59% more annual energy output than ground-mounted systems. The newly developed regression models provide a predictive tool for estimating the operating temperature of solar PV installations above water bodies, using only three meteorological parameters: ambient temperature, solar insolation, and wind velocity, for accurate temperature prediction. [ABSTRACT FROM AUTHOR]

Byline: Sanjay Vashishtha, M. Ramachandran In the present study, we have evaluated the electricity savings by replacing consumers' existing appliances, typically low energy efficiency appliances, with improved energy efficiency appliances in a phased manner. The paper presents a demand side management (DSM) scenario and estimates annual energy and peak demand savings for the period (2002-2012). Savings have been estimated at end use level, using a computer model known as the long-range energy alternative planning model (LEAP). The result indicates that, until 2012, the DSM programmes can reduce future peak power demand by 700MW and energy demand by 22.8 billion kWh. In addition the DSM plan also reduces CO2 emissions by 34.7 million tonnes, compared to expected growth without DSM (Reference scenario). This would further avoid capacity expansion in the transmission and distribution network due to reduced energy and power requirements of consumers.