Optimizing Load Dispatch using Artificial Neural Networks and Fractional Weighted Models

The paper describes a protocol that will optimize the real-time load data collected from regional load dispatch centres in India with a hybrid Artificial Neural Network (ANN) and Fractional Weighted Load Dispatch (FWLD) approach. The flow is data input, fractional calculation (using fractional calculus to incorporate alpha memory and non-locality of time), ANN module training, and making real-time dispatch decisions. The referred model, S,T,D,C,T (Supply, Transmission, Demand, Cost, and Time) efficiently tracks real-time fluctuations of S,T,D,C,T to optimize energy allocation. This method outperforms traditional ones in load distribution and flexibility with grid oscillations. Experimental studies have reported the values of Mean Squared Error (MSE) of 245.80 MW² during training, 260.95 MW² during testing, with Root Mean Squared Errors (RMSE) of 15.68 MW and 16.15 MW during testing; Mean Absolute Percentage Error (MAPE) values also were recorded, being less than 10.35%, showing better predictability. This combination of ANN learning and fractional weighting methods gives more stability, efficiency, and scalability for present power system optimization.