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首页> 外文期刊>Energy and Buildings >A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction
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A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction

机译：建筑用电预测的混合教学人工神经网络

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摘要

Numerous data-driven models have been successfully adopted for electrical energy consumption forecasting at building and larger scales. When the data set for forecasting is multi-sourced, heterogeneous or inadequate, single data-driven model may lead to convergence problem or poor model accuracy. The combination of advanced evolutionary algorithms (EAs) and data-driven models is proved effective in terms of prediction accuracy and robustness improvements. However, some of them are very time consuming to converge. In this paper, a novel EA, i.e. teaching learning based optimization (TLBO), is proposed for short-term building energy usage prediction. To enhance its convergence speed and optimization accuracy, the basic TLBO algorithm is further modified in three aspects. The improved algorithm is combined with artificial neural networks (ANNs) and applied to hourly electrical energy prediction of two educational buildings located in USA and China respectively. Performance comparisons show that the proposed model has superior performances than previously reported GA-ANN and PSO-ANN methods in terms of convergence speed and predictive accuracy, and is suitable for online energy prediction in the future. (C) 2018 Elsevier B.V. All rights reserved.
机译：许多数据驱动的模型已成功地用于建筑物和更大规模的电能消耗预测。当用于预测的数据集是多源的，异构的或不足时，单个数据驱动的模型可能会导致收敛问题或模型准确性差。事实证明，先进的进化算法（EA）和数据驱动模型的组合在预测准确性和鲁棒性改进方面非常有效。但是，其中一些收敛非常耗时。本文提出了一种新颖的EA，即基于教学学习的优化（TLBO），用于短期建筑能耗预测。为了提高其收敛速度和优化精度，在三个方面对基本TLBO算法进行了进一步修改。改进后的算法与人工神经网络（ANN）相结合，分别应用于美国和中国两座教育建筑的每小时电能预测。性能比较表明，该模型在收敛速度和预测精度方面均优于以前报道的GA-ANN和PSO-ANN方法，并且适合将来的在线能量预测。 （C）2018 Elsevier B.V.保留所有权利。

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