Missing Data Imputation of an Off‐Grid Solar Power Model for a Small‐Scale System
Bibliographic record
Abstract
A small scale off-grid solar power system is installed on the roof-top. Its parameters are sensed and recorded. There might be a case of logging missing or erroneous data. The sensors might be faulty or environmental factors may affect the solar panel to cause logging of inaccurate data. Hence, the data prediction algorithm is employed to replace the missing/erroneous values in the logged data set. The predictions made by each algorithm are compared and analyzed to find the most accurate algorithm for data prediction. The data is imputed using Case-Based Algorithm (CBR) and Multiple Imputation by Chained Equation (MICE) techniques. CBR uses past experiences to analyse the data while MICE runs the data in multiple iterations to impute the missing values. The results from both the methods are analyzed and compared. The latter method gives lower values of errors compared to the former method. The Mean Square Error from CBR is 0.259 while from MICE it is 0.107. The Root Mean Square Error from CBR is 0.499 while of MICE it is 0.322. The result shows that the MICE algorithm performs better than the CBR algorithm for imputing the lost values.
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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 | 0.001 | 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".