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WhatsApp or info This project presents a study of short-term hourly load forecasting using Artificial Neural Networks (ANNs).To demonstrate the effectiveness of the proposed approach, data from Jodhpur State Load Dispatch and Communication Center, Rajasthan Vidyut Parasaran Nigam (JVN). The forecasted load is compared with the actual load and average percentage error is calculated. Three months data of JVN collected for training and test data to carry out forecasting load demand is given in the appendix. The following four cases are investigated to validate the proposed methodology based on hourly load demand for the full day (24 hours) and the daily temperature, humidity and wind speeds.
Reference Paper-1: Short-Term Load Forecasting Using Artificial Neural Network Techniques
Author’s Name: Muhammad Buhari, and Sanusi Sani Adamu
Source: IMECS
Year: 2012
Reference Paper-2: Short-Term Load Forecasting Using Artificial Neural Network Techniques
Author’s Name: Shady Mahmoud Elgarhy, Mahmoud M. Othman, Adel Taha, and Hany M. Hasanien
Source: IEEE
Year: 2017
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