Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling

Lu, Siyue; Zhang, Baoqun; Ma, Long; Xu, Hongyao; Li, Yuantong; Yang, Shaobing

doi:10.3390/en16135035

Cited by 1 publication

(1 citation statement)

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“…In this study, we have employed five common model evaluation metrics: mean squared error (MSE), root mean square error (RMSE), coefficient of determination (R 2 ), mean absolute percentage error (MAPE), and mean absolute error (MAE) to comprehensively assess the performance of the model that we have proposed. These metrics each offer unique advantages and are applicable in different domains, collectively forming a comprehensive evaluation of the model's efficacy [41,42]. This multi-metric approach facilitates a deeper understanding of the model's performance under various circumstances, providing a more accurate depiction of its strengths and weaknesses.…”

Section: Model Evaluation Indexmentioning

confidence: 99%

Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis

Li,

Shen

et al. 2023

Energies

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The sustainability and efficiency of the wind energy industry rely significantly on the accuracy and reliability of wind speed forecasting, a crucial concern for optimal planning and operation of wind power generation. In this study, we comprehensively evaluate the performance of eight wind speed prediction models, spanning statistical, traditional machine learning, and deep learning methods, to provide insights into the field of wind energy forecasting. These models include statistical models such as ARIMA (AutoRegressive Integrated Moving Average) and GM (Grey Model), traditional machine learning models like LR (Linear Regression), RF (random forest), and SVR (Support Vector Regression), as well as deep learning models comprising ANN (Artificial Neural Network), LSTM (Long Short-Term Memory), and CNN (Convolutional Neural Network). Utilizing five common model evaluation metrics, we derive valuable conclusions regarding their effectiveness. Our findings highlight the exceptional performance of deep learning models, particularly the Convolutional Neural Network (CNN) model, in wind speed prediction. The CNN model stands out for its remarkable accuracy and stability, achieving the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the higher coefficient of determination (R2). This underscores the CNN model’s outstanding capability to capture complex wind speed patterns, thereby enhancing the sustainability and reliability of the renewable energy industry. Furthermore, we emphasized the impact of model parameter tuning and external factors, highlighting their potential to further improve wind speed prediction accuracy. These findings hold significant implications for the future development of the wind energy industry.

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Section: Model Evaluation Indexmentioning

confidence: 99%