Residential energy management systems with renewables and battery energy storage
Bibliographic record
Abstract
With increasing concern towards the environmental impact of energy production, distribution, and consumption in the modern world, the overall energy landscape is changing. This Master’s Thesis investigates methods of addressing these inevitable transformations through the incorporation of renewable energy and energy storage on the residential-scale using energy management systems (EMSs). A simulated residential house model was developed in order to compare a variety of different energy management techniques on the same basis. The simulated EMS investigation has covered: deterministic EMSs, those in their most basic forms; adaptive EMSs, utilizing machine learning and predictive control algorithms; and, a transactional EMS. The deterministic EMSs produced the least annual cost savings, but are the simplest to implement. Adaptive EMSs have shown the highest estimated cost savings, with increased controller complexity as a trade-off. The transactive EMS has shown intermediate cost savings, with additional potential benefits such as demand response and community integration capabilities. Experimental work has been conducted verifying critical claims of the systems, focusing on battery output control and inter-agent controller communication. The most interesting areas warranting future research involve implementing predictive control experimentally – and on a wider scale – and investigating transactive control on the community level.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".