Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm | JoVE Visualize

Area of Science:

Computational Intelligence
Energy Systems Analysis
Time Series Forecasting

Background:

Accurate forecasting of renewable energy generation and electricity consumption is crucial for grid stability and management.
Conventional optimization algorithms for time series models often face limitations in adaptability and robustness due to fixed search patterns.
The need for advanced optimization techniques to improve the performance of forecasting models in dynamic energy markets.

Purpose of the Study:

To introduce and evaluate a novel auto-evolution hyper-heuristic algorithm, AE-GAPB, for optimizing time series forecasting models.
To enhance the accuracy and efficiency of forecasting electricity generation and consumption by leveraging adaptive optimization.
To demonstrate the superiority of AE-GAPB over existing optimization algorithms in real-world energy datasets.

Main Methods:

Development of the AE-GAPB algorithm, integrating a genetic algorithm (GA) at the high-level and particle swarm optimization (PSO) and bat algorithm (BA) at the low-level.
The GA dynamically optimizes hyperparameters for PSO and BA, guided by prediction accuracy.
Adaptive evolution of GA's crossover and mutation rates based on iteration time and fitness values for enhanced adaptability.

Main Results:

AE-GAPB demonstrated significant improvements in prediction accuracy for renewable energy generation and electricity consumption forecasting.
The auto-evolutionary approach substantially accelerated the optimization process compared to conventional methods.
Validation on six time series models using Japanese energy datasets (Hokkaido, Kyushu, Tohoku) confirmed AE-GAPB's excellent performance.

Conclusions:

AE-GAPB offers a robust and adaptive solution for optimizing complex time series forecasting models in the energy sector.
The proposed hyper-heuristic approach effectively addresses the limitations of traditional optimization algorithms.
AE-GAPB provides a promising tool for enhancing the accuracy and efficiency of renewable energy and electricity consumption predictions.