Techno-Economic Analysis of Decentralised Small Hydro Power Projects for Rural Electrification: A Case Study of Meenvallom Project in Kerala with the Use of RET Screen Expert Advanced Software
Bibliographic record
Abstract
Rural electrification is a fundamental factor of poverty mitigation and rural growth of a nation. In India, electricity has not played an active role in the socio-economic progress of the village. Gross Domestic Product (GDP) is growing by 8%, whereas the involvement of agriculture division is 1.9%. The government of India has encouraged the goal of providing electricity to all villages in various phases. Therefore, the aim of this research was to define the techno-economic analysis of decentralized Small Hydro Power projects in Kerala with the help of RET Screen expert software. The objectives are to explore the potential hydropower to be harvested by Small Hydro Power for rural electrification, to assess financial saving and cost analysis and to investigate the environmental impact of the Small Hydro Power in Kerala. The RET Screen expert is used to assess the economic feasibility of a Small Hydro Power system and to recommend the possibility of installing the technology in the Meenvallom SHP in Kerala. The RET Screen expert is a Clean Energy Management Software developed by the Government of Canada, which is a free-of-charge decision support software for pre-feasibility or feasibility analysis and also ongoing energy efficiency performance analysis (Netscreen Canada). An attempt has been made in this study is to evaluate the features of rural electrification in Kerala and the viability of Small Hydro Power (SHP).
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".