Paper Citation Record · LEDGER

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.11390.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.11390 v1

Coverage vector

measured 37 of 37 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation ab4d5109-d6d7-4a6e-ba55-2be113f44fa2 · outbound

This paper cites Probabilistic time series forecast- ing with recurrent neural networks for intermittent demand.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Probabilistic time series forecast- ing with recurrent neural networks for intermittent demand

Reference 1

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Observation e8d8442e-b2d8-411b-86f7-1f03afe4af18 · outbound

This paper cites Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Reference 2

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Observation 7ffb3b82-1e1d-414a-9f0c-11101576f77e · outbound

This paper cites Mapping properties of Fourier transforms, revisited.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Mapping properties of Fourier transforms, revisited

Reference 3

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Observation 9217e437-d535-412d-90de-2522c9f38b1a · outbound

This paper cites Tsfpaper: A reposi- tory of time series forecasting papers.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Tsfpaper: A reposi- tory of time series forecasting papers

Reference 4

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Observation f155a3ba-7633-4faf-8b45-6fa5bfcb1353 · outbound

This paper cites Staff report on data needs for electricity system planning.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Staff report on data needs for electricity system planning

Reference 5

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Observation 6f3f901b-e818-47b8-b761-06b3b65ff188 · outbound

This paper cites The m4 forecasting competition—a practitioner’s view.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting The m4 forecasting competition—a practitioner’s view

Reference 6

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Observation 3a5f7adc-4819-4a3c-ba68-a09ec5b9c1f9 · outbound

This paper cites Monash Time Series Forecasting Archive.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Monash Time Series Forecasting Archive

Reference 7

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Observation e2713ab1-75fb-45b2-b51f-7464d3507f3c · outbound

This paper cites Simple versus complex forecasting: The evidence.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Simple versus complex forecasting: The evidence

Reference 8

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Observation 0c0b6909-ce7f-40b8-9cc6-7bf7d86be9f2 · outbound

This paper cites Analysis and clustering of residential customers energy behavioral patterns using robust data mining techniques.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Analysis and clustering of residential customers energy behavioral patterns using robust data mining techniques

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.885621Z

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Observation b208b789-5dca-4a5b-9d84-3ad3f16d7ef1 · outbound

This paper cites A data-driven approach for the disaggregation of building-sector heating and cooling loads from hourly utility load data.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting A data-driven approach for the disaggregation of building-sector heating and cooling loads from hourly utility load data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.873752Z

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Observation 6b0009e1-342b-4c40-a292-6d6e64a475b4 · outbound

This paper cites Large-scale unusual time series detection.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Large-scale unusual time series detection

Reference 11

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Observation c56b1ca9-1b9b-436f-9339-fac8471c56d8 · outbound

This paper cites Criteria for classifying forecasting methods.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Criteria for classifying forecasting methods

Reference 12

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Observation 225d67f8-15b7-4ccd-97cc-eca884fd4873 · outbound

This paper cites Renewable Energy-Storage Systems Integration in Power Grids: Modeling, Control and Optimization.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Renewable Energy-Storage Systems Integration in Power Grids: Modeling, Control and Optimization

Reference 13

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Observation fc902af1-b517-4a91-af9d-036d92186e99 · outbound

This paper cites Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.805097Z

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Observation df1bb087-950d-4788-a318-dd70eaa17a07 · outbound

This paper cites Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

Reference 15

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Observation 6ee17a3a-b00c-4454-afec-b55a15fc9ed4 · outbound

This paper cites Modeling long- and short-term temporal patterns with deep neural networks.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Modeling long- and short-term temporal patterns with deep neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.772859Z

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Observation 6c8f67f0-989e-467b-8a0f-04443d791cc5 · outbound

This paper cites Renewable energy and demand forecasting in an integrated smart grid.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Renewable energy and demand forecasting in an integrated smart grid

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.755524Z

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Observation bd61b50b-9b26-4be0-9680-15f4a4bea8f2 · outbound

This paper cites Deep learning for electricity market forecasting: Current methods, challenges and opportunities.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Deep learning for electricity market forecasting: Current methods, challenges and opportunities

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.741616Z

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Observation ad62ad97-f85b-4fa2-8d29-3a74e62fa1a3 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Efficiently Modeling Long Sequences with Structured State Spaces

Reference 19

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Observation 5c33a452-780e-4eb4-adc7-d52baa8cab97 · outbound

This paper cites Temporal fusion transformers for interpretable multi-horizon time series forecasting.International Journal of Forecasting, 37(4):1748–1764, 2021.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Temporal fusion transformers for interpretable multi-horizon time series forecasting.International Journal of Forecasting, 37(4):1748–1764, 2021

Reference 20

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Observation fe0e5c2b-d51b-431d-92f0-45d6f3ea8995 · outbound

This paper cites Statistical and machine learning forecasting methods: Concerns and ways forward.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Statistical and machine learning forecasting methods: Concerns and ways forward

Reference 21

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This paper cites The m5 competition: Background, organization, and implementation.International Journal of Forecasting, 38(4):1325– 1336, 2022.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting The m5 competition: Background, organization, and implementation.International Journal of Forecasting, 38(4):1325– 1336, 2022

Reference 22

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Observation f75a42a0-3692-44f7-a29f-17901ae39bc7 · outbound

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IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Fforma: Feature-based forecast model averaging

Reference 23

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This paper cites Reliability guideline: Methods for establishing resource adequacy requirements.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Reliability guideline: Methods for establishing resource adequacy requirements

Reference 24

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Observation 0fe88528-f2e8-42c3-9ea0-48f3ae00a013 · outbound

This paper cites Computing electricity spot price predic- tion intervals using quantile regression and forecast averaging.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Computing electricity spot price predic- tion intervals using quantile regression and forecast averaging

Reference 25

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Observation 3eea1c2e-e1ac-4b43-9e2e-29c8f1d7f448 · outbound

This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 26

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IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Forecasting: theory and practice

Reference 27

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Observation f417cf98-8def-4ff0-b9a1-62c0eb14e00b · outbound

This paper cites Deep state space models for time series forecasting.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Deep state space models for time series forecasting

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.641310Z

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Observation c8a241fa-18d9-4b12-870c-2b2e0dd1ce7a · outbound

This paper cites Probabilistic load forecasting for large-scale distributed energy resources aggregation.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Probabilistic load forecasting for large-scale distributed energy resources aggregation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.629256Z

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Observation 4e49e707-c994-4fb7-8fe7-af0a192b2c1b · outbound

This paper cites Machine learning based adaptive fault diagnosis consid- ering hosting capacity amendment in active distribution network.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Machine learning based adaptive fault diagnosis consid- ering hosting capacity amendment in active distribution network

Reference 30

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Observation 924a1a41-366a-4d30-b7f6-d542bc74769a · outbound

This paper cites Deepar: Probabilistic forecasting with autoregressive recurrent networks.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Deepar: Probabilistic forecasting with autoregressive recurrent networks

Reference 31

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Observation ecd868d4-40e1-4878-929a-29a8493e32da · outbound

This paper cites Financial time series forecasting with deep learning: A systematic literature review: 2005–.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Financial time series forecasting with deep learning: A systematic literature review: 2005–

Reference 32

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Observation 184bf1b2-82bb-4a3c-9c2d-60df4e92c84c · outbound

This paper cites Review of smart meter data analytics: Applications, methodologies, and challenges.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Review of smart meter data analytics: Applications, methodologies, and challenges

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.567933Z

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Observation 453189ad-e444-42f4-b8b2-2a4afb3b5afa · outbound

This paper cites Electricity price forecasting: A review of the state-of-the-art with a look into the future.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Electricity price forecasting: A review of the state-of-the-art with a look into the future

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.555365Z

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Observation 81484f04-9184-4127-a9e3-bf48b42e9d22 · outbound

This paper cites Autoformer: De- composition transformers with auto-correlation for long-term series forecasting.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Autoformer: De- composition transformers with auto-correlation for long-term series forecasting

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:50.542566Z

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This paper cites all-at-once.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting all-at-once

Reference 36

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Observation d91b7e19-cf1b-4cff-b40f-0c046a3e9808 · outbound

This paper cites an unresolved cited work.

IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting Unresolved cited work

Reference 2019

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