The accurate demand prediction with high efficiency and advanced demand-side controller are essential for the enhancement of energy flexibility provided by buildings, whereas the current literature fails to present the mechanism on modelling development and demand-side control. This paper aims to deal with the complexity of building demand prediction with supervised machine learning method, including the multiple linear regression, the support vector regression and the backpropagation neural network. The regularization, adding the sum of the weights to the learning function, is utilized to improve the training speed and to solve the overfitting by eliminating the unnecessary connections with small weights. The configuration of the artificial neural network was presented, and sensitivity analysis has been conducted on the learning performance regarding different training times. Energy flexibilities of sophisticated building energy systems (including renewable system, electric and thermal demands and building services systems) were quantitatively characterised with a series of quantifiable indicators. Moreover, several advanced controllers have been developed and contrasted, in regard to the flexibility utilisation of building energy systems. Results showed that, the developed hybrid controller with short-term prediction through the cross-entropy function is more technically competitive than other controllers. With the implementation of the developed hybrid controller, the peak power of the grid importation can be reduced from 500.3 to 195 kW by 61%. This study formulates a data-driven model with an advanced machine learning algorithm for the accurate building demand prediction and a hybrid advanced controller with short-term prediction for the energy management, which are critical for the promotion of energy flexible buildings.

高效准确的需求预测和先进的需求侧控制器对于增强建筑物提供的能源灵活性至关重要，而当前文献未能提供建模开发和需求侧控制的机制。本文旨在利用有监督的机器学习方法来应对建筑需求预测的复杂性，包括多元线性回归，支持向量回归和反向传播神经网络。通过将权重之和添加到学习函数中的正则化可通过消除权重较小的不必要连接来提高训练速度并解决过拟合问题。提出了人工神经网络的配置，对不同训练时间的学习表现进行了敏感性分析。复杂的建筑能源系统（包括可再生系统，电力和热力需求以及建筑服务系统）的能源灵活性通过一系列可量化的指标进行了定量表征。此外，就建筑能源系统的灵活性利用而言，已经开发和对比了几种先进的控制器。结果表明，开发的具有交叉熵功能的短期预测的混合控制器在技术上比其他控制器更具竞争力。通过使用已开发的混合控制器，电网输入的峰值功率可以从500.3降低到195 kW，降低61％。