Bibliographic record
Abstract
With the popularity of smart electrical appliances and home energy management systems, there have been a massive amount of data generated about the electricity consumption. This data can be beneficial for the utility companies as it provides the behaviour patterns of customers, and thus useful decisions can be made to optimize the load on the grid. In this work, we propose and implement a two-way communication system between the transformer agent (TA), attached to a neighbourhood’s electric transformer, and its customer agents (CAs), attached to each house in that neighbourhood. Once data is collected at the TA, it is communicated to the utility which can control and suggest any changes in consumption behaviours. In our system, CAs form a self-healing mesh network with the TA using IP-based Wi-Fi, while TAs communicate with the utility headquarters using the LTE network. Our system is implemented in compliance with the IEEE 2030.5 standard requirements, also known as smart energy profile 2.0. We have performed several tests across the Carleton University campus. We have also tested and implemented this system in real neighbourhoods in Ottawa, including Sandcherry and Viewmount sites to prove the system’s operation and reliability.The data obtained from the communication system is stored in a database hosted by IBM Cloud services. Our aim in this work is not only to communicate the data but ii to further process it and help the utility companies design better demand side management (DSM) programs to ensure efficient transmission and distribution of energy. This solves the problem of balancing electric demand and supply at the grid and also reduces peak demands, which helps lower the electricity bills for the consumers. In this context, we analyze the household electricity consumption data to forecast energy consumption for short-term (hours/days ahead) and long-term (weeks/months ahead). To this end, we use and compare seven different machine learning models predicting the energy consumption: linear regression, polynomial regression, support vector regression (SVR) using linear kernel, SVR using Gaussian kernel(SVR-G), SVR using the polynomial kernel, feed-forward neural networks (FFNN), and recurrent neural networks (RNN) using long-short-term memory (LSTM) neurons. 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 R2also known as the coefficient of determination. We then propose a novel approach for short-term load forecasting by combining the power of multiple models and evaluate its performance on a real energy consumption dataset that is publicly available by Massachusetts Institute of Technology (MIT). Results show that our proposed model performs better than existing models for time series energy forecasting.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".