Forecasting and optimisation for microgrid in home energy management systems
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 wide proliferation of renewable energy and deregulation of power grid systems require small power utilization systems to deploy intelligent methods of adjustment to the user power demand. To accomplish this goal, the smart power demand forecasting and power consumption optimization methods and algorithms need to be developed. For this purpose, small power utilization systems can benefit from the techniques developed for the smart grid in general. The present paper is devoted to the development of a forecasting model based on the Long Short‐Term Memory ( LSTM ) method and an optimization model based on Genetic Algorithm ( GA ) adopted for the use in home energy management systems ( HEMS ). The present work describes a smart microgrid architecture with a focus on LSTM and GA . The experiments demonstrate that the developed algorithms generate a stable pattern of daily power demand. The use of the developed algorithms allows automated shifting of power to achieve the lowest price without sacrificing their comfort. The main contributions of the present work are the inclusion of all parts of the smart microgrid architecture (non‐invasive load identification, forecasting, optimization, renewable energy sources and storage elements) in the research proposing a fully automated control in HEMS rather than recommendation based only.
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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