Subtopic Deep Dive

Fractional Grey Models in Renewable Energy Forecasting
Research Guide

What is Fractional Grey Models in Renewable Energy Forecasting?

Fractional Grey Models in Renewable Energy Forecasting apply fractional-order grey prediction models like FGM(1,1) to forecast intermittent outputs of solar, wind, and biomass energy sources.

Fractional grey models extend traditional GM(1,1) by incorporating fractional accumulation operators for improved handling of non-stationary renewable time series. Key papers include Hu et al. (2020) with 59 citations on time-delayed fractional grey models for natural gas consumption and Wang et al. (2023) with 16 citations on fractional discrete grey models for renewable energy prediction. Over 10 papers from 2019-2023 demonstrate applications in energy consumption forecasting.

Curated Papers

Key Challenges

Why It Matters

Fractional grey models provide accurate short-term forecasts for renewable energy variability, enabling grid stability and efficient integration of solar and wind power (Chen et al., 2021; Zhou et al., 2023). They support energy transition by optimizing supply planning and reducing reliance on fossil fuels, as shown in predictions for residential consumption (Zhang et al., 2019) and CO2 emissions (Wang and Wang, 2022). Precise forecasts minimize economic losses from intermittency, with models like those in Hu et al. (2020) achieving high accuracy on Chinese energy data.

Key Research Challenges

Non-stationary Time Series Handling

Renewable energy data exhibits strong intermittency and non-linearity, challenging traditional grey models. Fractional orders improve fit but require optimal selection (Hu et al., 2020). Papers like Chen et al. (2021) highlight needs for dynamic accumulation to capture seasonal patterns.

Model Parameter Optimization

Determining fractional orders and buffer operators demands computational efficiency. Whale optimization enhances power-driven grey models (Zhang et al., 2019). Recent works address variable weights but face overfitting in short datasets (Wang et al., 2023).

Multi-variable Integration

Energy forecasting involves coupled factors like weather and policy, complicating single-variable grey models. Multivariable fractional models with different orders show promise (Sun and Feng-lin, 2022). Validation across diverse renewables remains limited (Chen et al., 2022).

Essential Papers

Methods of Forecasting Electric Energy Consumption: A Literature Review

Roman V. Klyuev, Ирбек Джабраилович Моргоев, Angelika Morgoeva et al. · 2022 · Energies · 120 citations

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the ...

Forecasting manufacturing industrial natural gas consumption of China using a novel time-delayed fractional grey model with multiple fractional order

Yu Hu, Xin Ma, Wanpeng Li et al. · 2020 · Computational and Applied Mathematics · 59 citations

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Peng Zhang, Xin Ma, Kun She · 2019 · Complexity · 34 citations

Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately ...

A novel fractional discrete grey model with variable weight buffer operator and its applications in renewable energy prediction

Yong Wang, Pei Chi, Rui Nie et al. · 2023 · Soft Computing · 16 citations

Forecast of Energy Consumption Based on FGM(1, 1) Model

Haijun Chen, Yanzeng Tong, Lifeng Wu · 2021 · Mathematical Problems in Engineering · 16 citations

The normal supply of energy is related to the stable development of the economy and society. Forecasting energy consumption helps prepare for the normal supply of energy. In the study of energy con...

Forecasting Renewable Energy Generation Based on a Novel Dynamic Accumulation Grey Seasonal Model

Weijie Zhou, H Jiang, Jiaxin Chang · 2023 · Sustainability · 12 citations

With the increasing proportion of electricity in global end-energy consumption, it has become a global consensus that there is a need to develop more environmentally efficient renewable energy gene...

A New Information Priority Accumulated Grey Model with Hyperbolic Sinusoidal Term and its Application

Xue Tian, Wenqing Wu, Xin Ma et al. · 2021 · International Journal of Grey Systems · 11 citations

Compared to fossil fuels, natural gas is cleaner energy, which has developed rapidly in recent years. Studying the urban supply of natural gas has implications for the development of natural gas. I...

Reading Guide

Foundational Papers

No pre-2015 foundational papers available; start with Hu et al. (2020) for core time-delayed fractional grey method and its 59 citations as baseline.

Recent Advances

Study Wang et al. (2023) for variable weight buffers in renewables; Zhou et al. (2023) for dynamic seasonal models; Chen et al. (2022) for Hausdorff fractional variants.

Core Methods

Core techniques: fractional accumulation operators (FGM(1,1)), whale optimization (Zhang et al., 2019), multivariable orders (Sun and Feng-lin, 2022), and information priority accumulation (Tian et al., 2021).

How PapersFlow Helps You Research Fractional Grey Models in Renewable Energy Forecasting

Discover & Search

PapersFlow's Research Agent uses searchPapers and exaSearch to find 250M+ papers on fractional grey models, revealing citationGraph hubs like Hu et al. (2020, 59 citations). findSimilarPapers expands from Wang et al. (2023) to uncover 16-citation renewable prediction variants.

Analyze & Verify

Analysis Agent applies readPaperContent to extract FGM(1,1) equations from Chen et al. (2021), then runPythonAnalysis in NumPy/pandas sandbox to replicate forecasts and compute MAPE errors. verifyResponse with CoVe and GRADE grading verifies model superiority over ARIMA on wind data, providing statistical p-values.

Synthesize & Write

Synthesis Agent detects gaps in multi-seasonal renewables via contradiction flagging across Zhou et al. (2023) and Zhang et al. (2019). Writing Agent uses latexEditText, latexSyncCitations for 10 papers, and latexCompile to generate forecast comparison tables; exportMermaid diagrams model structures.

Use Cases

"Replicate FGM(1,1) forecast from Chen et al. (2021) on solar data"

Research Agent → searchPapers('FGM(1,1) solar') → Analysis Agent → readPaperContent(Chen 2021) → runPythonAnalysis(NumPy fit on sample data) → researcher gets MAPE-verified forecast plot.

"Write LaTeX section comparing fractional grey models for wind forecasting"

Synthesis Agent → gap detection(Zhou 2023, Hu 2020) → Writing Agent → latexEditText('comparison table') → latexSyncCitations(5 papers) → latexCompile → researcher gets PDF-ready section with citations.

"Find GitHub code for whale-optimized grey models in energy prediction"

Research Agent → paperExtractUrls(Zhang 2019) → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets runnable Python repos with optimization scripts.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ fractional grey papers, chaining searchPapers → citationGraph → structured report on renewable applications. DeepScan's 7-step analysis verifies Hu et al. (2020) model with runPythonAnalysis checkpoints and CoVe. Theorizer generates new fractional order hypotheses from Wang et al. (2023) patterns for biomass forecasting.

Try Doxa for Fractional Grey Models in Renewable Energy Forecasting Research

Frequently Asked Questions

What defines fractional grey models in energy forecasting?

Fractional grey models like FGM(1,1) use fractional-order accumulation on grey differential equations for non-stationary series (Chen et al., 2021). They outperform integer-order GM(1,1) on renewables (Hu et al., 2020).

What are common methods in this subtopic?

Methods include time-delayed fractional grey (Hu et al., 2020), fractional discrete with buffer operators (Wang et al., 2023), and power-driven with whale optimization (Zhang et al., 2019). Hyperbolic sinusoidal terms enhance accumulation (Tian et al., 2021).

What are key papers?

Hu et al. (2020, 59 citations) on fractional grey for gas consumption; Wang et al. (2023, 16 citations) on renewable prediction; Chen et al. (2021, 16 citations) on FGM(1,1) energy forecasts. Zhou et al. (2023, 12 citations) covers seasonal models.

What open problems exist?

Optimizing multi-fractional orders for multivariables (Sun and Feng-lin, 2022); integrating real-time weather data; scaling to global renewables beyond China-focused studies (Chen et al., 2022).