Data Communication and Analytics for Smart Grid Systems
Bibliographic record
Abstract
With the popularity of smart electrical appliances and home energy management systems, there has been a massive amount of data generated by the power consumption. This data can be beneficial for the utility as it provides the behavior patterns of customers, and thus useful decisions can be made to optimize the load on the grid. In this work, we establish a bidirectional communication system between some homes through the customer agents (CAs), which are installed at home, and the transformer agent (TA) which is installed at the local transformer. Once data is collected at the TA, it is sent to the cloud through LTE. We then use IBM Cloud services to filter and analyze this data to forecast energy consumption and make recommendations to different customers based on their real-time changing behaviors. To this end, we use six different machine learning models predicting the energy consumption: support vector regression (SVR) using linear kernel, SVR using Gaussian kernel, SVR using the polynomial kernel, linear regression, polynomial regression, and feed- forward neural networks (FFNN). To measure the accuracy of these models, we compute three different error metrics, the normalized mean absolute percentage error (NMAPE), the normalized root mean square error (NRMSE), and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> also known as the coefficient of determination. Based on these results, we observe that the performance of the forecasting model depends on the dataset properties including the size and variations. For example, while linear and polynomial regressions perform well for small-scale datasets, FFNN gives higher accuracy for large-scale datasets.
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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".