BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

So, Dayeong; Oh, Jinyeong; Jeon, Insu; Moon, Jihoon; Lee, Miyoung; Rho, Seungmin

doi:10.3390/systems11090456

Cited by 14 publications

(3 citation statements)

References 52 publications

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“…The statistical significance of the proposed model's superiority was validated through the Friedman test, which was performed separately for each dataset and evaluation metric. The Friedman test is a nonparametric statistical test employed to identify discrepancies in performance across a range of models or conditions [33]. In the DKASC-ASA-1A dataset, the p-value for MAE was 1.54 × 10 −5 , and the RMSE p-value was 6.14 × 10 −6 .…”

Section: Comprehensive Ablation Study and Performance Analysismentioning

confidence: 99%

A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer

Moon

2024

Electronics

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Due to environmental concerns about the use of fossil fuels, renewable energy, especially solar energy, is increasingly sought after for its ease of installation, cost-effectiveness, and versatile capacity. However, the variability in environmental factors poses a significant challenge to photovoltaic (PV) power generation forecasting, which is crucial for maintaining power system stability and economic efficiency. In this paper, a novel muti-step-ahead PV power generation forecasting model by integrating single-step and multi-step forecasts from various time resolutions was developed. One-dimensional convolutional neural network (CNN) layers were used for single-step forecasting to capture specific temporal patterns, with the transformer model improving multi-step forecasting by leveraging the combined outputs of the CNN. This combination can provide accurate and immediate forecasts as well as the ability to identify longer-term generation trends. Using the DKASC-ASA-1A and 1B datasets for empirical validation, several preprocessing methods were applied and a series of experiments were conducted to compare the performance of the model with other widely used deep learning models. The framework proved to be capable of accurately predicting multi-step-ahead PV power generation at multiple time resolutions.

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Section: Comprehensive Ablation Study and Performance Analysismentioning

confidence: 99%

A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer

Moon

2024

Electronics

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“…Prediction models must have high accuracy for renewable energy management, particularly for solar power generation. Hybrid DL models have emerged as a significant advancement in this field; they combine various DL approaches to enhance prediction accuracy [38][39][40]. This innovation addresses the limitations of existing sequential learning models, which struggle with incomplete data and fail to capture both spatial and temporal patterns effectively.…”

Section: Introductionmentioning

confidence: 99%

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

Min,

Hong,

Song

et al. 2024

Electronics

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We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This model uses a self-attention-based temporal convolutional network (TCN) to process and predict PV outputs with high precision. We perform meticulous data preprocessing to ensure accurate data normalization and outlier rectification, which are vital for reliable PV power data analysis. The TCN layers are crucial for capturing temporal patterns in PV energy data; we complement them with the teacher forcing technique during the training phase to significantly enhance the sequence prediction accuracy. By optimizing hyperparameters with Optuna, we further improve the model’s performance. Our model incorporates multi-head self-attention mechanisms to focus on the most impactful temporal features, thereby improving forecasting accuracy. In validations against datasets from nine regions in South Korea, SolarFlux outperformed conventional methods. The results indicate that SolarFlux is a robust tool for optimizing PV systems’ management and operational efficiency and can contribute to South Korea’s pursuit of sustainable energy solutions.

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“…The complexity of this challenge stems from the diversity of grapes, encompassing variations in morphology, color, size, and varietal characteristics influenced by different growing environments [3]. Traditional deep learning approaches falter in addressing this intricate and variable scenario [4].…”

Section: Introductionmentioning

confidence: 99%

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

Peng,

Ouyang,

Peng

et al. 2024

Sensors

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Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

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BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

Cited by 14 publications

References 52 publications

A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer

A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

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