Adaptive neuro‐fuzzy based solar cell model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The modelling of photovoltaic (PV) solar cells using a hybrid adaptive neuro‐fuzzy inference system (ANFIS) algorithm is presented. It is based on the decomposition of the cell output current into photocurrent and junction current. The photocurrent is linearly dependent on solar irradiance and cell temperature; consequently, its analytical computation is done easily. However, the junction current is highly non‐linear and depends on cell voltage and temperature. Therefore, its analytical computation is complicated and the manufacturers do not supply any information about this parameter. Moreover, there is no way to measure it physically. Therefore, it is proposed to use the ANFIS algorithm as a powerful technique in order to estimate this current and reconstruct the output PV cell current using the photocurrent. The model validation is based on the gradient descent and chain rule applied to a set of data different than the one used for training process. The advantage of the proposed model is that only one climatic parameter is used as the input to the ANFIS algorithm, which makes it less sensitive to climatic variations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it