Case study of residential energy management systems with solar PV, wind and battery energy storage
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it