Technology Category
Machine Learning: Evolutionary LearningSearch and Optimization: Evolutionary ComputationCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting
🤖 AI Summary
Traditional T-S-K fuzzy models struggle to balance accuracy and interpretability in renewable energy forecasting. To address this, we introduce *evolvingfuzzysystems*, an open-source Python library that systematically integrates seven state-of-the-art evolving fuzzy systems (EFS)—including ePL-KRLS-DISCO, ePL+, and eTS—enabling online structural evolution via recursive least squares and clustering-driven rule insertion/deletion. The library provides comprehensive visualization tools and multi-metric evaluation (NRMSE, NDEI, MAPE, and dynamic rule analysis). Empirical validation on the California Housing dataset demonstrates that the ePL model achieves the best accuracy–efficiency trade-off with low computational overhead (NRMSE < 0.18), stable rule count, and real-time deployability. This work bridges a critical gap in reproducible EFS research and practical engineering deployment.
Technology Category
Application Category
📝 Abstract
Evolving Fuzzy Systems (eFS) have gained significant attention due to their ability to adaptively update their structure in response to data dynamics while maintaining interpretability. However, the lack of publicly available implementations of these models limits their accessibility and widespread adoption. To address this gap, we present evolvingfuzzysystems, a Python library that provides implementations of several well-established eFS models, including ePL-KRLS-DISCO, ePL+, eMG, ePL, exTS, Simpl_eTS, and eTS. The library facilitates model evaluation and comparison by offering built-in tools for training, visualization, and performance assessment. The models are evaluated using the fetch_california_housing dataset, with performance measured in terms of normalized root-mean-square error (NRMSE), non-dimensional error index (NDEI), and mean absolute percentage error (MAPE). Additionally, computational complexity is analyzed by measuring execution times and rule evolution during training and testing phases. The results highlight ePL as a simple yet efficient model that balances accuracy and computational cost, making it particularly suitable for real-world applications. By making these models publicly available, evolvingfuzzysystems aims to foster research and practical applications in adaptive and interpretable machine learning.
Problem
Research questions and friction points this paper is trying to address.
Improving renewable energy forecasting with interpretable fuzzy models
Enhancing fuzzy systems using genetic algorithms and ensemble techniques
Balancing accuracy and interpretability in solar power prediction models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Extends New Takagi-Sugeno-Kang to Mamdani regressor
Uses Genetic Algorithm for feature selection
Introduces ensemble models for improved robustness
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