Energy Consumption Forecasting in Net-Zero Energy Buildings: Firefly-Driven LSTM for Smart Consumer Electronics and Edge-Based Energy Optimization

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.

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