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REVIEW 4 major objections 4 minor 67 references

NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper claims that genetic and ensemble fuzzy inference systems, especially GEN-NTSK and RF-NTSK, match or beat deep learning on photovoltaic forecasting while keeping rules interpretable.

desk verdict Clear model idea, shipped code, but the empirical claim is contradicted by the paper's own tables; evaluation protocol is too weak to support superiority. read the letter →

arxiv 2506.06285 v3 pith:PNCKEJB2 submitted 2025-04-28 cs.AI

Kaike Sa Teles Rocha Alves , Eduardo Pestana de Aguiar This is my paper · ORCID
classification cs.AI
keywords fuzzyinferencesystemstimeseriesforecastingTakagi-Sugeno-KangmodelsMamdaniregressorgeneticalgorithmfeatureselectionensemblelearningphotovoltaicpowerinterpretability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a family of data-driven fuzzy inference systems can give renewable-energy forecasters an interpretable alternative to deep learning without sacrificing accuracy. It extends the New Takagi-Sugeno-Kang (NTSK) model into a Mamdani-style regressor whose rules are cut from the target variable's own range, and then wraps that regressor in a genetic-algorithm feature selector and in ensembles. On four photovoltaic time series from two plants, the paper reports that the genetic and ensemble variants, especially GEN-NTSK and RF-NTSK, often post lower errors than classical machine-learning and deep-learning baselines while using just a handful of rules. If the claim holds, solar plant operators would get forecast accuracy comparable to deep learning from rule sets they can read, trained in far less time.

What carries the argument

The load-bearing mechanism is the rule-generation scheme shared by NTSK and the new NMR: the target variable's observed range is split into $R_{\max}$ equally spaced intervals, each training sample is assigned to a rule by the interval its target value falls in, and Gaussian membership functions are fit to the antecedent attributes within each rule. The consequent of a rule is this target interval itself, so a rule reads as a statement of the form 'when the inputs resemble this cluster, the next-step power falls in this range'. The genetic wrapper evaluates candidate feature subsets by the fuzzy model's error, and the ensemble variants R-NMR, R-NTSK, and RF-NTSK average models built on randomized attribute subsets; RF-NTSK combines the random forest and R-NTSK outputs with weights inversely proportional to their training errors.

What would settle it

Re-run the four photovoltaic series with a fixed train/validation/test split, tune every model only on the validation set, and report errors over several random seeds; if GEN-NTSK and RF-NTSK no longer sit near the top of the NRMSE, NDEI, and MAPE tables, the comparative claim fails. A second check is whether the GA-selected feature subsets and final rule counts stay stable across seeds, since unstable selection would undercut the interpretability benefit.

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Extended reading notes

Core claim

The central claim is that the proposed fuzzy models, in particular Genetic New Takagi-Sugeno-Kang (GEN-NTSK) and Random Forest New Takagi-Sugeno-Kang (RF-NTSK), achieve error metrics that match or beat both traditional machine learning and deep learning on the tested photovoltaic series while producing a simpler, rule-based structure. The paper reports that on the Alice Springs and Yulara datasets, GEN-NTSK (wRLS) and R-NTSK/RF-NTSK frequently appear among the lowest normalized root-mean-square errors, and the best fuzzy models often tie or exceed the best non-fuzzy baselines, which are LS-SVM among classical models and CNN or GRU among deep models. The rule-design mechanism, splitting the target's range into equally spaced intervals, assigning samples to rules by where their target falls, and fitting Gaussian fuzzy sets from those partitions, gives the user direct control over the number of rules and makes each consequent interpretable as a target-variation interval rather than a polynomial.

Load-bearing premise

The central performance claims rest on the assumption that the grid search used to set hyper-parameters was run on training or validation data only and never on the test portion that produced the reported error tables; the paper does not state the data split, the random seeds, or the number of repeated runs.

Editorial extensions

If this is right

  • A practitioner who needs an interpretable solar-forecasting model can choose GEN-NTSK or RF-NTSK and expect errors near the best deep-learning results on daily photovoltaic series.
  • Because the number of rules is a user-set hyperparameter, the same implementation can produce anything from a one-rule baseline to a nineteen-rule model, making the accuracy-interpretability tradeoff explicit.
  • RF-NTSK's error-weighted blend of random forest and R-NTSK gives a reusable recipe for combining a tree ensemble with a fuzzy regressor.
  • The genetic wrapper can shrink the input set, as in the reported Alice 1A case where six of twelve attributes were selected, which the paper links to better interpretability and reduced overfitting risk.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the interval-based rule generation is not solar-specific, so the same models should transfer to other one-step-ahead forecasting targets such as wind power, electricity load, or prices; this is directly testable with the released library.
  • Beyond the paper: because the paper reports no variance across runs, a repeated-seed benchmark would reveal whether GEN-NTSK's edge is stable or partly tuning luck.
  • Beyond the paper: the un-evaluated NMR classifier sketched in the paper, one rule per class, could give an interpretable fuzzy classifier, but its accuracy is unknown.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes a new Mamdani fuzzy regressor (NMR) and extends the authors' earlier New Takagi-Sugeno-Kang (NTSK) model with genetic-algorithm feature selection and ensemble variants (GEN-NMR, GEN-NTSK, R-NMR, R-NTSK, RF-NTSK). The models are described mathematically, and an experiment is reported on four photovoltaic power datasets (Alice 1A, Alice 38, Yulara 1, Yulara 5) comparing NRMSE, NDEI, and MAPE against a wide range of classical, deep-learning, and evolving-fuzzy baselines. The abstract and conclusion claim that the proposed genetic and ensemble fuzzy models, especially GEN-NTSK and RF-NTSK, achieve superior performance and often outperform traditional ML and DL models, while maintaining interpretability. The paper also reports rule-based interpretation via a table of extracted rules and provides a Python library (nfisis).

Significance. If the reported superiority were established, the paper would offer a practical interpretable alternative to deep learning for PV forecasting. The model formulations are simple and the release of the nfisis library is a useful contribution. However, the central empirical claim is not supported by the data presented in the paper and is in fact contradicted by the authors' own tables: on every dataset a baseline model yields the lowest NRMSE. Because the experimental protocol lacks an explicit validation split and reports only single point estimates without seeds or error bars, the comparative performance claims cannot be regarded as reliable. The contribution therefore reduces to a modest algorithmic variation whose claimed advantage is unsubstantiated.

major comments (4)
  1. [Abstract; Tables 3–6] The central claim that GEN-NTSK and RF-NTSK 'achieve superior performance' and 'often outperform' traditional ML/DL models is contradicted by the paper's own results. In Table 3 (Alice 1A) the best NRMSE is eTS (0.20639) versus GEN-NTSK(wRLS) 0.20711; in Table 4 (Alice 38) exTS achieves 0.20443 versus RF-NTSK 0.21087; in Table 5 (Yulara 1) CNN achieves 0.16568 versus RF-NTSK 0.18351; in Table 6 (Yulara 5) ePL-KRLS-DISCO achieves 0.20501 versus GEN-NTSK(wRLS) 0.20625. Thus no proposed model obtains the best NRMSE on any dataset, and the abstract's superiority claim fails at face value.
  2. [Section 4.1, Tables 3–6] The hyperparameter optimization is described as a 'grid search to achieve the lowest possible error' (Section 4.1) without stating that a validation split was used; this leaves open the possibility that test data were used for model selection. The tables report single point estimates with no seeds, repeated runs, or error bars. With daily PV series of roughly 730 points, differences of 0.001–0.02 NRMSE are likely within run-to-run noise, so even the ordering among close methods is not established. The absence of a specified evaluation protocol invalidates the comparative performance claims.
  3. [Section 4.3/4.5 and Conclusion] The Discussion section (Section 4.5) acknowledges that CNN and LS-SVM achieved the lowest errors among DL and classical models, and the Conclusion restricts the claim to 'GEN-NTSK and RF-NTSK obtained the lowest error among the proposed models.' This is inconsistent with the abstract's claim of outperforming ML/DL models. The paper should either remove the superiority claim or provide evidence that the proposed models are competitive when evaluated with a proper validation protocol.
  4. [Section 3.1.1, Eqs. (1)–(3)] Equation (3) assigns samples to rule index floor((y_k - y_underline)/IS), which yields 0 when y_k equals the minimum y_underline; since rules are indexed 1 to Rmax, this is an off-by-one error that would misassign the minimal-target sample. The authors should correct the indexing or clarify the boundary condition. While this is a localized technical issue, it affects the formal definition of the proposed NMR model.
minor comments (4)
  1. [Section 1] The sentence 'Finally, Section 5 concludes de paper and proposes future works' contains a typo ('de' should be 'the'), and the sentence 'The models are applied to renewable energy datasets' is repeated verbatim a few lines later.
  2. [Section 3.2] The description of R-NMR/R-NTSK ensemble construction is incomplete: 'the best-performing model from this subset is added to the ensemble' does not specify which data (training or validation) are used to judge the best performer, nor how diversity is maintained across iterations.
  3. [Abstract and Section 1] The term 'NFISiS' is used both as the model family name and as the name of the released library (nfisis); the authors should clarify whether NFISiS refers to the set of models or specifically to the software package.
  4. [References] The reference for 'Regression Tree' cites only 'Breiman et al.' without a year or full publication details; this should be completed to match the other references.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the derivations are standard supervised regression and the empirical claims are benchmarked against external models.

full rationale

No circular step could be exhibited. The proposed NMR (Section 3.1) assigns training samples to rules by intervals of the observed target value (Eqs. 1-3), then predicts by a weighted average of per-rule consequent means (Eq. 6); this is ordinary supervised regression, not a definition that presupposes the predicted quantity. The GA wrapper (Section 3.1.3) and the ensemble weighting (Eq. 7) likewise use training errors and model outputs, and the resulting forecasts are evaluated on the same external benchmark datasets as the baselines. The authors' citations to their own NTSK papers supply background and the base NTSK construction, which is summarized in Section 2; the paper's central performance claim is not derived from those citations but is testable against Tables 3-6. In fact, those tables show a baseline model attaining the lowest NRMSE on every dataset, so the abstract's 'superior performance' claim is empirically contradicted rather than circularly entailed. The concern that grid-search hyperparameters may have been selected on the test set is a validity threat, but the paper only says the search targets 'the lowest possible error' (Section 4.1) without specifying the split; without evidence of the split, this cannot be counted as a fitted input renamed as a prediction under the hard rules.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The model itself introduces no free physical constants or invented entities. The free parameters are algorithmic hyperparameters chosen during experiments, and the main burden is the unstated evaluation protocol plus the target-interval rule construction assumption.

free parameters (3)
  • Rmax, the number of rules for each fuzzy model = not reported per dataset
    The user sets Rmax and the paper says grid search selected it for the lowest error, but the chosen values are not reported in the results tables.
  • Genetic algorithm parameters = unspecified
    Population size, number of generations, crossover rate, and mutation rate are not reported, yet the genetic feature selection results depend on them.
  • Ensemble size or number of inducers z = unspecified
    The random ensemble description says z models are trained in R-NMR and R-NTSK, but the paper does not state the value used in the experiments.
assumptions (3)
  • domain assumption Target-based rule assignment produces meaningful rules.
    Samples are assigned to rules by the target value in Section 3.1.1, Equation 3, and no comparison with clustering-based or input-space rule generation is provided.
  • domain assumption Gaussian membership functions with per-rule mean and standard deviation are adequate for all rules.
    Step 5 of Section 3.1.1 invokes Gaussian fuzzy sets without discussing degenerate standard deviations when a rule has very few samples or poor input-space coverage.
  • ad hoc to paper Grid-search hyperparameter selection uses only training data.
    Section 4.1 states hyperparameters are optimized to achieve the lowest error but does not describe a validation split; the reported superiority depends on this unstated assumption.

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Cite this review

Pith. "Pith review of NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting." pith.science (2026). https://pith.science/paper/PNCKEJB2

@misc{pith2026250606285,
  author       = {Pith},
  title        = {Pith review of: NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PNCKEJB2}},
  note         = {Machine review of arXiv:2506.06285}
}
read the original abstract

Deep learning models, despite their popularity, face challenges such as long training times and a lack of interpretability. In contrast, fuzzy inference systems offer a balance of accuracy and transparency. This paper addresses the limitations of traditional Takagi-Sugeno-Kang fuzzy models by extending the recently proposed New Takagi-Sugeno-Kang model to a new Mamdani-based regressor. These models are data-driven, allowing users to define the number of rules to balance accuracy and interpretability. To handle the complexity of large datasets, this research integrates wrapper and ensemble techniques. A Genetic Algorithm is used as a wrapper for feature selection, creating genetic versions of the models. Furthermore, ensemble models, including the Random New Mamdani Regressor, Random New Takagi-Sugeno-Kang, and Random Forest New Takagi-Sugeno-Kang, are introduced to improve robustness. The proposed models are validated on photovoltaic energy forecasting datasets, a critical application due to the intermittent nature of solar power. Results demonstrate that the genetic and ensemble fuzzy models, particularly the Genetic New Takagi-Sugeno-Kang and Random Forest New Takagi-Sugeno-Kang, achieve superior performance. They often outperform both traditional machine learning and deep learning models while providing a simpler and more interpretable rule-based structure. The models are available online in a library called nfisis (https://pypi.org/project/nfisis/).

Figures

Figures reproduced from arXiv: 2506.06285 by the authors.

Figure 1
Figure 1. Flowchart presenting the learning phase for NTSK [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Flowchart of the learning phase for NMR where v represents the mean of the fuzzy set and σ is the standard deviation. Gaussian membership functions are advantageous because they are smooth, non-zero over all points, and effectively represent linguistic variables with precision and clarity Azimi and Miar-Naimi [2020]. 3.1.2 The Inference Process for NMR - Test Phase First, the input data is fuzzified using the respec… view at source ↗
Figure 3
Figure 3. Graphic of predictions for Alice 1A [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Graphic of predictions for Alice 38 [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Graphic of predictions for Yulara 1 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Graphic of predictions for Yulara 5 all models, and eMG had 372 final rules, the second-highest ones [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.