Solar PV and Biomass Resources-Based Sustainable Energy Supply for Off-Grid Cellular Base Stations
Bibliographic record
Abstract
Due to the technological revolution and higher user data demand, the telecommunication industry is expanding at an exponential rate. Fulfilling the increasing demand of energy for the rising cellular networks has become a great challenge to the network operators because of the limited reservation of fuel energy sources and the growing concern about global warming. Energy harvesting (EH) from renewable energy sources (RES) has become an overwhelming initiative to minimize energy deficiency and carbon footprints. This paper investigates the feasibility of solar photovoltaic (PV) and biomass resources based hybrid supply systems for powering the off-grid Long Term Evolution (LTE) cellular macrocell base stations (BSs) in Bangladesh focusing the technical, economic and environmental issues. In addition, the green energy sharing technique has been incorporated via a low resistive path for optimal use of RES. The proposed system has enough potential to achieve long term sustainability and reduction of pollution rates by fulfilling the future energy demand of BS. In this work, Hybrid Optimization Model for Electric Renewables (HOMER) simulation-based feasibility analysis is used to assess the optimal system, energy production, total net present cost (NPC), cost of electricity (COE) and greenhouse gas (GHG) emission depending on different system parameters. Furthermore, the performance of the network has been evaluated in terms of throughput and energy efficiency using Matlab-based Monte Carlo simulations. Results demonstrate that the proposed hybrid renewable energy powered BSs would be a reliable and longer-lasting green solution for the telecom sector while maintaining the quality of service (QoS). Finally, an extensive comparison with other systems has also been done to justify network validity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".