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Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.

Mauro Castelli1, Leonardo Trujillo2, Leonardo Vanneschi1

  • 1NOVA IMS, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal.

Computational Intelligence and Neuroscience
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Summary
This summary is machine-generated.

Accurate energy consumption forecasting (ECF) is crucial for utilities. This study introduces a semantic genetic programming framework that improves prediction accuracy and reduces costs for electric utilities.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Energy consumption forecasting (ECF) is vital for economic policy and utility operations.
  • Inaccurate ECF leads to increased operating costs for electric utilities.
  • Existing methods require improvement for enhanced prediction accuracy.

Purpose of the Study:

  • To propose a novel semantic-based genetic programming framework for accurate energy consumption forecasting.
  • To develop a system capable of generating near-optimal predictions on unseen data.
  • To enhance the efficiency and accuracy of ECF models.

Main Methods:

  • Integration of a semantic genetic programming approach with a local search method.
  • Utilizing semantic genetic operators for enhanced exploration capabilities.
  • Employing a local searcher for exploitation and refinement of solutions.

Main Results:

  • The proposed framework achieves high probability of finding near-perfect solutions.
  • Experimental results demonstrate superior performance compared to state-of-the-art techniques.
  • The system produces lower prediction errors on the same dataset.
  • Forecasting accuracy on unseen data is significantly improved.

Conclusions:

  • The semantic-based genetic programming framework is highly suitable for energy consumption forecasting.
  • Combining semantic genetic programming with local search accelerates the search process.
  • The enhanced models provide accurate forecasts, even for previously unseen data.
  • This approach offers a significant advancement in energy consumption prediction.