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Solar Panels Plus Farming? Agrivoltaics Explained

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How to predict time dependent variables with the help of neural networks ?

Objective: To predict the probability of rainfall based on monthly mean data of rainfall, runoff and evapotranspiration collected from a single gauge station for the last fifty years Data Preprocessing : The fifty years mean monthly data of Rainfall,Runoff and Evapotranspiration is available.The data values of the variables is ranked in a descending way where best or minimum rank is assigned to the largest value and worst or maximum rank was given to smallest value of the variable.The probability of each of the data value of the variables can be calculated with the help of the following formula : P = m/(n+1) The data thus converted become dimensionless and thus normalization is not followed. If the data values have dimensions it is better to remove the scale from the same. This will make the job of the neural network program faster and easier. Input Variables of the Present Study : Input to the model will be Probability of Runoff and Evapotranspiration. Output Variable of the Present S
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Five Models Traditionally Used For Electrical Load Predictions : Issue 1 of WENexus Letters

Load forecasting is a technique used by power companies to predict the power or energy needed to balance the supply and load demand at all times. A load model is a mathematical representation of the relation between the demand and supply of power to a system, specifically, power generating units like thermal or hydropower plant. Based on the forecasted load, the mechanism to generate power is adjusted. This way power plant conserves the fuel used in power generation and also minimize the cost of running the plant(i.e., operational cost). This edition of WENexus Letters is dedicated to 'load prediction models' which are extremely important for sustaining the profitability of any type of power plant. Content 1)Five Models Traditionally Used For Electrical Load Predictions 2)Which model to select if you can use only one? 3)Further Reading: Three best sources for learning about load forecasting methods 4)Recent Techniques in Load Prediction Models: Artificial Neural Networks 5)Call
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