Optimizing electric load forecasting with support vector regression/LSTM optimized by flexible Gorilla troops...

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Data Science

Background:

• Accurate electric load forecasting is crucial for efficient energy management and grid stability.
• Existing forecasting models face challenges in precision and adaptability to dynamic energy consumption patterns.

Purpose of the Study:

• To enhance the precision and effectiveness of electric load forecasting models.
• To integrate the adaptive capabilities of the Gorilla Troops optimization algorithm into a Support Vector Regression and Long Short-Term Memory (SVR/LSTM) framework.

Main Methods:

• A hybrid SVR/LSTM model was developed and optimized using a flexible Gorilla Troops algorithm.
• The methodology was validated using a comprehensive dataset from 200 residential properties in Texas, including electricity consumption and meteorological data.
• Performance was benchmarked against established contemporary load forecasting techniques.

Main Results:

• The modified SVR/LSTM model demonstrated superior performance compared to existing methods.
• The proposed approach achieved higher accuracy and robustness in electric load demand forecasting.
• Empirical findings were enhanced by the use of authentic, diverse datasets.

Conclusions:

• The Gorilla Troops-optimized SVR/LSTM model offers a significant advancement in electric load forecasting.
• The methodology provides a more accurate and robust solution for predicting energy demand.
• This research contributes a valuable tool for optimizing energy management strategies.