热需求密度短期预测的单变量和多变量策略比较研究:探索单一和混合深度学习模型

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sajad Salehi , Miroslava Kavgic , Hossein Bonakdari , Luc Begnoche
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引用次数: 0

摘要

要实现最佳的建筑能源管理、节约成本、环境可持续性和负责任的能源消耗,就需要对供暖能源需求进行准确的短期预测。此外,短期供暖能源预测还有助于在寒冷气候条件下实现零能耗建筑性能。鉴于短期预测在供热能源管理中的极端重要性,本研究评估了六种常用的深度学习算法来预测能源负荷,包括单一模型和混合模型。总体而言,使用卷积神经网络的混合模型是表现最好的预测模型,无论它们是多元模型还是单变量模型。然而,虽然多元模型在第一个小时内表现较好,但在最后 24 小时内,单变量模型往往更为准确。因此,第一个时间步表现最好的预测模型是多元混合卷积神经网络-循环神经网络模型,其决定系数(R²)为 0.98,平均绝对误差最小。然而,对最终时间步预测效果最好的是单变量混合模型卷积神经网络-长短期记忆,其 R² 为 0.80。此外,与单变量模型相比,表现最佳的多元混合模型的预测准确率每小时下降得更快。这些发现表明,多元模型可能更适合早期时间步预测,而单变量模型可能更适合后期时间步预测。因此,组合模型可以提高不同时间步的准确性,从而实现高保真预测,为能源管理提供全面的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density: Exploring single and hybrid deep learning models

Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density: Exploring single and hybrid deep learning models

Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management, cost savings, environmental sustainability, and responsible energy consumption. Furthermore, short-term heating energy prediction contributes to zero-energy building performance in cold climates. Given the critical importance of short-term forecasting in heating energy management, this study evaluated six prevalent deep-learning algorithms to predict energy load, including single and hybrid models. The overall best-performing predictors were hybrid models using Convolutional Neural Networks, regardless of whether they were multivariate or univariate. Nevertheless, while the multivariate models performed better in the first hour, the univariate models often were more accurate in the final 24 h. Thus, the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination (R²) of 0.98 and the lowest mean absolute error. Yet, the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R² of 0.80. Also, the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models. These findings suggest that multivariate models may be better suited for early timestep predictions, while univariate models may be better suited for later time steps. Hence, combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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