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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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Invitation to Act as a Reviewer for a special issue of Journal of Water and Energy Nexus

I will like to invite my readers to act as a reviewer of the articles submitted for possible publication in the special issue entitled "Optimization of Water Energy Nexus by Soft Computation and Hierarchical Decision Making Methods" in the Journal of Water and Energy Nexus. This journal is a new journal which is published from Energy in Style Publishers.Although this is a new journal but it is not a paid or predatory journal and perform extensive peer reviews before allowing an article to be published in the journal. The Abstract and other details of the Special Issue is as given below : Abstract In any operational systems both water and energy is utilized for the sustenance of the benefits received from the system. The nexus of water and energy and their interdependency and cognitive utilization of the mutuality between the two basic essentials of mankind ensure the success of the system, which become evident from the output received from the setup. Acc
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