Energy Consumption Forecasting in Net-Zero Energy Buildings: Firefly-Driven LSTM for Smart Consumer Electronics and Edge-Based Energy Optimization
Bibliographic record
Abstract
Net-Zero Energy Buildings (NZEBs) are present-day constructions capable of generating sufficient clean energy for their consumption. Since these buildings tend to rely on clean energy from Renewable Energy Sources (RESs), there is a challenge of controlling when and how much energy is generated. Thus, energy consumption prediction is essential to manage supply and demand using RESs and energy storage solutions. However, considering that the underlying electrical appliances have uncertain and non-linear energy consumption patterns, effectively predicting energy consumption is a tedious task. Accordingly, the existing prediction schemes, such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Long-Short-Term Memory (LSTM), face various problems like vanishing gradients, limited memory cells, and fixed-length inputs. To overcome these issues, in this study, we propose a hybrid framework that combines the LSTM and Firefly (FF) optimization algorithms. LSTM learns complex long-term dependencies and efficiently predicts energy variations. In addition, the standard FF is modified (thereafter referred to as modified FF, MFF) to address existing issues, such as premature convergence and limited global search in complex time-series data. More specifically, FF is modified with additional components, such as chaotic logistic maps, adaptive inertia weight, and levy flight. These components generate an initially diverse population of fireflies, adjust attractiveness parameters, regulate local and global exploration capabilities, and accelerate local search by creating new best solutions. Due to these modifications, the proposed combination of LSTM and MFF converges faster, requires fewer iterations, and requires less processing time. For a comprehensive evaluation of the proposed work, the simulation results are analyzed using the Portuguese house time-series dataset. The results prove the efficacy of the proposed methodology as compared to the existing schemes. Optimized prediction of energy consumption can be used to synchronize energy supply and demand, as well as to facilitate green buildings to create a more sustainable environment. The results demonstrate the effectiveness of the proposed methodology, achieving an RMSE of 23.55 W and an R² of 0.99, thereby surpassing existing approaches. Moreover, the proposed method can be integrated with smart consumer electronics and edge devices, enabling real-time, energy-efficient decision-making at the edge for enhanced control in NZEBs and beyond.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".