Intelligent Energy Management for Multistorey Building With Photovoltaic-Based Electric Vehicle Charging Infrastructure
Bibliographic record
Abstract
The usage of electric vehicles (EVs) has increased and it leads to additional demand along with existing residential demand and managing it becomes challenging. Further EV charging systems that function during the daytime in multistorey buildings expedite the peak loading. The main objective of this work is to minimize the operating cost of the system and conversion losses. In this work, the microgrid incorporated with a bidirectional converter plays a major role in dc-ac and ac-dc conversion. The photo voltaic (PV) sources support the system with sufficient dc power generation and batteries store the dc power and supply the load in case of insufficiency. By utilizing a genetic algorithm (GA) and appropriate energy management (EM) to charge EVs according to time-of-use tariff patterns, the impact of growing demand on the grid is greatly mitigated. To ease the burden on the grid during peak hours, the interruptible loads are shifted to off-peak times. Other challenges of EV charging such as energy saving, maximum peak demand, voltage instability, and high current drawing issues are rectified and well presented with existing topology. When compared to the standard scheme, the energy savings in the proposed topology are much increased, reaching 33.04%, while the cost reduction is 57.27%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".