Climate-Adaptive Residential Demand Response Integration with Power Quality-Aware Distributed Generation Systems: A Comprehensive Multi-Objective Optimization Framework for Smart Home Energy Management
Bibliographic record
Abstract
Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".