Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
Sensors (Basel). 2019 Feb 10;19(3):721. doi: 10.3390/s19030721.
The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors.
能源和物联网领域技术的进步，导致低压本地微电网的电气环境日益复杂，电动汽车、微发电和本地存储的扩展也是如此。为了实现社区层面的分散式本地能源系统，需要建立一个家庭能源管理系统（HEMS），以有效地整合和管理家庭能源微发电、消耗和存储。在物联网技术的支持下，建立 HEMS 对于实现负荷平衡以及其他智能能源解决方案来说，进行国内电力需求预测是非常重要的。具有各种网络类型（例如，DNN、基于 LSTM/GRU 的 RNN）和其他配置的人工神经网络广泛应用于能源预测。然而，对于每个研究，网络配置的选择通常是通过经验或枚举方法逐个案例研究得出的。此外，常用的网络初始化方法根据随机数分配参数值，这会导致模型性能（包括学习效率、预测准确性等）的多样性。在本文中，提出了一种进化集成神经网络池（EENNP）方法，以自动实现具有适当配置和初始化组合的性能良好的网络群体。在实验研究中，探索了三种应用场景下的多个家庭的电力需求预测：优化潜在网络配置集、预测单个家庭的电力需求和填补缺失数据。研究了进化参数对模型性能的影响。实验结果表明，所提出的方法在考虑的场景中取得了更好的解决方案。使用 EENNP 优化的潜在网络配置集可达到与手动优化相似的结果。家庭需求预测和缺失数据填充的结果优于简单的预测方法。
