Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios | Paper Citation Record

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Kim, S.-B.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Kim, S.-B

Reference 3

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Salinas, V.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Salinas, V

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Observation ca73e42c-37dc-455a-a402-5bc1d7e6f463 · outbound

This paper cites Aryandoust, A.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Aryandoust, A

Reference 7

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites URL http://data.europa.eu/eli/reg/2016/679/oj.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios URL http://data.europa.eu/eli/reg/2016/679/oj

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This paper cites Kezunovic, L.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Kezunovic, L

Reference 10

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Albrecht, openMeter data platform (2024).

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Albrecht, openMeter data platform (2024)

Reference 12

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This paper cites Meier, C.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Meier, C

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites R¨ as¨ anen, M.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios R¨ as¨ anen, M

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This paper cites Yilmaz, J.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Yilmaz, J

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Silipo, P.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Silipo, P

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This paper cites Riedl, M.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Riedl, M

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Gretton, K.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Gretton, K

Reference 21

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Li, Energy consumption forecasting with deep learning, J.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Li, Energy consumption forecasting with deep learning, J

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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This paper cites Singh, A.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Singh, A

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This paper cites doi:10.3390/en11020452.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios doi:10.3390/en11020452

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Muralitharan, R

Reference 34

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Rahman, V

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Attention Is All You Need

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Qureshi, M

Reference 38

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This paper cites Lai, W.-C.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Lai, W.-C

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 40

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 42

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This paper cites doi:10.3390/electronics12102175.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios doi:10.3390/electronics12102175

Reference 43

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 44

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 45

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This paper cites Arjovsky, S.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Arjovsky, S

Reference 46

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Observation bb4a998c-fdf2-4baa-af29-7083c11f3cad · outbound

This paper cites Gulrajani, F.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Gulrajani, F

Reference 47

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This paper cites Hochreiter, J.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Hochreiter, J

Reference 48

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Observation 9e86ec7b-8cd6-4b6a-9937-b6ce0d9e2b36 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Denoising Diffusion Probabilistic Models

Reference 49

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This paper cites Improved Denoising Diffusion Probabilistic Models.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Improved Denoising Diffusion Probabilistic Models

Reference 50

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 51

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This paper cites Braun, B.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Braun, B

Reference 52

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This paper cites Gabrielski, U.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Gabrielski, U

Reference 53

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Observation 6d964948-736c-4731-aef6-f71edf447a0f · outbound

This paper cites Hothorn, T.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Hothorn, T

Reference 54

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This paper cites Hothorn, L.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Hothorn, L

Reference 55

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This paper cites Masked Autoregressive Flow for Density Estimation.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Masked Autoregressive Flow for Density Estimation

Reference 56

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This paper cites Papamakarios, E.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Papamakarios, E

Reference 57

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Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 58

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This paper cites Fischer, A.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Fischer, A

Reference 59

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Source-reported events for the cited work

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Observation c70a7360-5020-4caf-a6a0-4ef9bb64aed2 · outbound

This paper cites Hehlert, B.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Hehlert, B

Reference 60

Resolution: verified exact; doi, observed 2026-08-06T22:58:52.900143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 95b0ac02-39a2-44fc-8f71-c7b055dd8bff · outbound

This paper cites an unresolved cited work.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:50.645937Z

Source-reported events for the cited work

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Observation 34f42d51-0424-4bef-9f1d-a4f27cd8d653 · outbound

This paper cites Federated Learning: Opportunities and Challenges.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Federated Learning: Opportunities and Challenges

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:50.713353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4de98db8-62e2-4f33-88cc-c1df7a7ae405 · outbound

This paper cites an unresolved cited work.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:50.781823Z

Source-reported events for the cited work

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Observation 2239b1b2-ed12-4f04-b53b-0f04ed65504d · outbound

This paper cites Entity Embeddings of Categorical Variables.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Entity Embeddings of Categorical Variables

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:50.833951Z

Source-reported events for the cited work

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Observation 4328bbaf-7fe3-4863-8f77-63c351f8f510 · outbound

This paper cites Goodfellow, J.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Goodfellow, J

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:50.925248Z

Source-reported events for the cited work

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Observation 04b4dfa8-3c46-4998-9647-fd3bc1b9c42b · outbound

This paper cites an unresolved cited work.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:58:58.985693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9ce49920-bf16-4220-93db-255112fee853 · outbound

This paper cites Lin, C.-H.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Lin, C.-H

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:58:58.746000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e4ad5a09-2afb-4ff3-8470-2fa64308cced · outbound

This paper cites Villani, et al., Optimal transport: old and new, Vol.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Villani, et al., Optimal transport: old and new, Vol

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.116380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51ff666d-2e2f-49a4-9bd7-0f302e365b43 · outbound

This paper cites Ronneberger, P.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Ronneberger, P

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.171363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f24c954-b506-420d-be1f-956823197924 · outbound

This paper cites Glow: Generative Flow with Invertible 1x1 Convolutions.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Glow: Generative Flow with Invertible 1x1 Convolutions

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.232223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f06c8ac-8b99-4984-a4ad-1d1825d18937 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios WaveNet: A Generative Model for Raw Audio

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.311808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a79894a-401c-495d-bca0-74a7eda3d907 · outbound

This paper cites Variational Inference with Normalizing Flows.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Variational Inference with Normalizing Flows

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.396768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44449725-3489-4bcf-a535-cf7e6790499b · outbound

This paper cites Normalizing Flows: An Introduction and Review of Current Methods.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Normalizing Flows: An Introduction and Review of Current Methods

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.453746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a69c2803-a3e9-441f-9a6a-8160d6d7277e · outbound

This paper cites Kneib, A.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Kneib, A

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.523960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8880156-1120-4982-8907-0db3a547fbc9 · outbound

This paper cites an unresolved cited work.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:58:58.538540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d0aff7e6-a96b-4f79-88cb-7b2c6b0ff2fa · outbound

This paper cites an unresolved cited work.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:58:58.397858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6decbbf-25f3-4ec9-a30c-bac5d8cfa24c · outbound

This paper cites Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:58:55.546946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5f64a945-6f4e-4cde-b762-78d65f378fe1 · outbound

This paper cites Deep and interpretable regression models for ordinal outcomes.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Deep and interpretable regression models for ordinal outcomes

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:58:52.690951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6f74ed87-f3a3-4900-995c-9618527834da · outbound

This paper cites Rugamer, P.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Rugamer, P

Reference 79

Resolution: malformed identifier; doi_truncated, observed 2026-08-06T22:58:52.427215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a01deef-2db6-4711-b766-602eedbe49c5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Adam: A Method for Stochastic Optimization

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.921762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99dd0a6a-2423-41ad-acb4-55b5f5b5a399 · outbound

This paper cites MADE: Masked Autoencoder for Distribution Estimation.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios MADE: Masked Autoencoder for Distribution Estimation

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:51.987436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1a5435c-6665-48f4-9254-3aa6808cadd2 · outbound

This paper cites Interpretable Neural Causal Models with TRAM-DAGs.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Interpretable Neural Causal Models with TRAM-DAGs

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:52.047463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4092d220-02ef-48c4-9f68-f36cb8f6ed56 · outbound

This paper cites Schreiber, Pomegranate: fast and flexible probabilistic modeling in python, Journal of Machine Learning Research 18 (164) (2018) 1–6.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Schreiber, Pomegranate: fast and flexible probabilistic modeling in python, Journal of Machine Learning Research 18 (164) (2018) 1–6

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:58:58.197750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 596b80a6-daa1-404b-8118-b04573f9faa2 · outbound

This paper cites Metric OM WGAN DDPM HMM MABF SLP ±SD sig.

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios Metric OM WGAN DDPM HMM MABF SLP ±SD sig

Reference 84

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T22:58:57.952646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers