A Review of Phase-Change Material-Based Thermal Batteries for Sustainable Energy Storage of Solar Photovoltaic Systems Coupled to Heat Pumps in the Building Sector
Bibliographic record
Abstract
Buildings account for about a third of global energy and it is thus imperative to eliminate the use of fossil fuels to power and provide for their thermal needs. Solar photovoltaic (PV) technology can provide power and with electrification, heating/cooling, but there is often a load mismatch with the intermittent solar supply. Electric batteries can overcome this challenge at high solar penetration rates but are still capital-intensive. A promising solution is thermal energy storage (TES), which has a low cost per unit of energy. This review provides an in-depth analysis of TES but specifically focuses on phase change material (PCM)-based TES, and its significance in the building sector. The classification, characterization, properties, applications, challenges, and modeling of PCM-TES are detailed. Finally, the potential for integrating TES with PV and heat pump (HP) technologies to decarbonize the residential sector is detailed. Although many studies show proof of carbon reduction for the individual and coupled systems, the integration of PV+HP+PCM-TES systems as a whole unit has not been developed to achieve carbon neutrality and facilitate net zero emission goals. Overall, there is still a lack of available literature and experimental datasets for these complex systems which are needed to develop models for global implementation as well as studies to quantify their economic and environmental performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".