Case study of residential energy management systems with solar PV, wind and battery energy storage
Bibliographic record
Abstract
As environmental concerns about energy production, distribution, and consumption rise, the energy landscape is evolving. This research examines methods to address these changes by integrating renewable energy and energy storage at the residential level using energy management systems (EMSs). A calibrated simulation residential house model was developed to consistently compare various energy management techniques. The study investigated 1) deterministic EMSs in their simplest forms, 2) adaptive EMSs utilizing machine learning and predictive control algorithms, and 3) a transactional EMS. Deterministic EMSs offered the lowest annual cost savings but were the easiest to implement. Adaptive EMSs provided the highest estimated cost savings but required more complex controllers. The transactional EMS yielded moderate cost savings and additional benefits such as demand response and community integration capabilities. Experimental work validated key system claims, focusing on battery output control and inter-agent controller communication deployed in practice on a local scale at the Archetype Sustainable House in Vaughan, Ontario, Canada. Future research should focus on implementing predictive control on a larger scale and exploring transactive control at 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".