Home ›
Entities
› academia
› Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts
Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts
Research article (Applied Energy, 2024) · cited 21× · AI/ML
Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts
Summary
Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts is a scholarly article[1].
Key Facts
Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts. Retrieved May 24, 2026, from https://4ort.xyz/entity/explainable-deeply-fused-nets-electricity-demand-prediction-model-factoring-climate-predictors-for-accuracy-and-deeper-i
MLA“Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/explainable-deeply-fused-nets-electricity-demand-prediction-model-factoring-climate-predictors-for-accuracy-and-deeper-i.
BibTeX@misc{4ortxyz_explainable-deeply-fused-nets-electricity-demand-prediction-model-factoring-climate-predictors-for-accuracy-and-deeper-i_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts}}, year = {2026}, url = {https://4ort.xyz/entity/explainable-deeply-fused-nets-electricity-demand-prediction-model-factoring-climate-predictors-for-accuracy-and-deeper-i}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts — https://4ort.xyz/entity/explainable-deeply-fused-nets-electricity-demand-prediction-model-factoring-climate-predictors-for-accuracy-and-deeper-i (retrieved 2026-05-24)
