Review of machine learning techniques for energy sharing and biomass waste gasification pathways in integrating solar greenhouses into smart energy systems
Bibliographic record
Abstract
• This review paper explores the integration of solar greenhouses into SESs through energy sharing and hydrogen production. • The paper examines how solar greenhouses contribute to energy efficiency by sharing heat and supplying biomass residues. • It investigates the role of ML and DL models in enhancing energy sharing and improving hydrogen production processes from biomass gasification. • The most common ML and DL models used in this area are categorized and described briefly. • The most recent studies in the scope of this review employing AI-based models are represented. The integration of solar greenhouses into smart energy systems (SESs) remains largely unexplored, despite their potential to enhance energy sharing and hydrogen production. This review investigates the role of solar greenhouses as active energy contributors within SESs, emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling (DHC) networks. A structured classification of machine learning (ML) and deep learning (DL) techniques applied in forecasting and optimizing these processes is provided. Additionally, the evolution of DHC systems is analyzed, with a focus on fifth-generation DHC (5GDHC) networks, which facilitate bidirectional energy exchange at near-ambient temperatures. The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses. A novel framework is proposed, illustrating their role as prosumers capable of exchanging electricity, hydrogen, and thermal energy within SESs. Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency, reduce carbon emissions, and improve system resilience. Furthermore, ML-driven predictive control strategies, particularly model predictive control (MPC), are identified as essential for optimizing real-time energy flows and biomass gasification processes. This study provides a foundation for future research on the technical, economic, and environmental feasibility of integrating greenhouses into SESs. The insights presented offer a pathway toward more sustainable, AI-driven energy-sharing networks, supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".