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Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

机译：基于混合自然启发式优化算法的建筑能耗预测建模

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Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight loT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities. (C) 2019 Elsevier B.V. All rights reserved.
机译：在过去的几十年中，由于人口，城市化和工业增长率的迅速提高，总体能源消耗有所增加。对能源的高需求导致单位能源成本更高，这可能会影响商业和住宅住宅的运营成本。因此，需要可以用于测量和优化构成现代建筑环境的互连物联网（IoT）设备和控制点的大型阵列的能源使用的更有效的预测技术。在本文中，我们提出了一种轻量级的loT框架，用于在局部水平上预测能源使用情况，以优化建筑范围内的能源分配政策。为此，可以使用自回归综合移动平均线（ARIMA）作为统计线性模型。但是，它无法对非平稳波动功耗数据中的动态非线性关系建模。因此，我们开发了一种基于ARIMA，支持向量回归（SVR）和粒子群优化（PSO）的改进的混合模型，以根据提供的数据预测精确的能耗。使用从控制建筑物中较大功能空间的环境执行器设备获取的功耗数据评估提出的模型。结果表明，所提出的混合模型在预测功耗方面优于其他替代技术。该方法因其对能耗的精确估算和轻量级的监控基础结构而适合于建筑能源政策的实施，从而可以降低能耗成本。此外，它提供了一种精确的工具，可以在智慧城市等较宽的建筑环境中优化能耗策略。 （C）2019 Elsevier B.V.保留所有权利。

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