Renewable Energy Integration into Industrial and Residential Buildings: A Study Across Urban, Rural, and Coastal Areas
Bibliographic record
Abstract
ABSTRACT Integrating renewable energy sources (RES) into buildings is one of the most important approaches to achieving sustainable energy systems. This paper presents a comprehensive study that evaluates the performance of RES such as photovoltaic (PV), wind, geothermal and biomass in different urban, rural, and coastal scenarios. In this paper, we analyze four types of buildings, including single‐family residential, multi‐family residential, commercial, and industrial, and evaluate the contribution of energy, supply and demand dynamics, and geographical influences on the performance of renewable energy (RE). Various results such as cost analysis and payback periods for different RESs, technical specifications, RES performance, state of charge (SoC) of the battery system, seasonal performance of RES in various geographic settings, carbon footprint of RES, and fossil fuel‐based power generation, supply chain risks, and resilience of RES technologies are obtained and discussed in detail. In addition, PV energy outperforms urban residential buildings due to its high availability on roofs. In coastal areas, wind energy can provide an acceptable amount of energy to industrial buildings. Biomass energy accounts for the lowest energy production in all buildings and locations. In all scenarios, geothermal energy can provide more consistent and sustainable baseload energy and complement the variable outputs of PV and wind. The results show that the interaction between RES provides a more reliable energy supply, reduces dependence on grid energy, and improves sustainability. This study emphasizes the importance of adapting the RE integration methods to the geographical and specific characteristics of the buildings. These results can provide better information for energy and building planners who want to use RE systems and achieve better environmental goals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".