Design, feasibility, performance assessment and optimization of the technical and financial viability of potential renewable energy and energy efficiency projects
Bibliographic record
Abstract
Until recently, renewable energy technologies (RETs) have been confronted with a huge up-front cost due to technologies in development. However, massive and global deployment of renewable energy systems has led to significant cost reductions and performance improvements and the hope is to see increasing uptake of RETs by African countries. Cameroon a central African country is heavily reliant on hydropower, which contributes an estimated 60% to the country’s total installed 1,400MW capacity in 2015. In addition, there is constants power failure due to the non-reliability of the electric grid and load shedding to meet increasing demand and or to relieve stress on the primary energy source when demand for electricity is greater than the primary power source can supply. However, climate change poses additional huge risk (large reservoirs and dams drying up) and to meet the increasing demand, Cameroon is being forced to seek alternative power sources. In January 2014, Cameroon welcomed its first 72MW solar photovoltaic plant as well as the construction of a 500MW solar photovoltaic park in Q3 of 2015 in the northern region of Cameroon. It is common practice that energy efficiency and energy conservation lead to environmental sustainability. “Energy security starts with energy efficiency and the most sustainable energy option or system is energy conservation”. While it is common practice that energy efficiency and energy conservation lead to environmental sustainability, the building envelope also plays a key role in energy usage, energy conservation and thermal comfort for the building occupants. The rate of infiltration/leakage rate detect the rate of energy usage and cost of using energy. Having a tide and well-constructed building envelope is a key factor and helps reduce/reconcile buildings gas and electricity consumption. RETScreen Expert is the most current version of the RETScreen software and was released to the public on September 19, 2016. The software allows for the comprehensive identification, assessment and optimization of technical and financial viability of potential renewable energy and energy efficiency projects; as well as the measurement and verification of the actual performance of facilities and the identification of energy savings/production opportunities [1-3]. This paper will demonstrate how the software can be use in the design, feasibility, performance assessment and optimization of technical and financial viability of potential renewable energy and energy efficiency projects related to photovoltaic, solar thermal, building envelope and the replacement of non/less-efficient electrical equipment with more efficient and cost effective lamps like LEDs and the installation of smart power bars with automatic shut off function for TV screens, to take care of phantom power in large scale hotels. This approach will further reduce electricity usage and help diversified the sources of energy for a country like Cameroon, which is often subjected to constant power failure and load shedding to either relieve the stress on a primary energy source when demand for electricity is greater than the primary power source can supply or to meet power supply during the critical seasons.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".