Verified Laboratory MATLAB Implementation

Electricity Price Forecasting using Optimized Neural Network

Predict electricity prices accurately using our MATLAB code with a tuned neural network! This model optimizes load and inclination factors for superior forec...

MATLAB Laboratory Run Demonstration 100% Tested
Electricity Price Forecasting using Optimized Neural Network
Environment: MATLAB R2024b / R2026 Compatible
Deliverables: .m scripts, .slx models, .mat data
Validation: Verified simulation plots & GUI App
Documentation: Full IEEE formatted project report

Project Methodology & Algorithm Details

This MATLAB code is for power value determining dependent on tuned loads and inclinations of neural system. The video for demo can be checked in the portrayal underneath.

 

Project Description

In a serious power market, the anticipating of vitality costs is a significant movement for all market members either for creating offering procedures or for settling on speculation choices. In this article, another guaging system is proposed for momentary expectation of the power value, which is a perplexing amount with nonlinear, unpredictable and time-subordinate conduct. Our figure system incorporates two oddities: another two-phase highlight determination calculation and another iterative preparing calculation. The element choice calculation has two sifting stages to eliminate unessential and repetitive applicant inputs, individually. This calculation depends on shared data and connection investigation. The improved iterative preparing calculation is made out of two neural systems in which the yield of the principal neural system is one of the contributions to the second. The general proposed methodology is applied to the Pennsylvania–New Jersey–Maryland (PJM) power showcases and contrasted and probably the latest value figure strategies.

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