VA-Creator—A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-09-20 DOI:10.1016/j.egyai.2024.100427
Michael Meiser , Benjamin Duppe , Ingo Zinnikus , Alexander Anisimov
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Abstract

With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning (ML), obtaining power consumption data is becoming more and more important. Collecting real-world energy data using sensors is time consuming, expensive, error-prone and in some situations not possible. Therefore, we present the VA-Creator, a framework to create Virtual Appliances (VAs). These VAs synthesize power consumption patterns (PCPs) based on Neural Networks (NNs) which adapt their architecture to the training data structure to simplify the creation of new VAs. To be able to generate all appliance types available in a typical household we use various kinds of NN, including Multilayer Perceptrons (MLPs), Long Short-Term Memorys (LSTMs) and a specific Generative Adversarial Network (GAN) as well as different ML techniques such as XGBoost, selecting the appropriate technique depending on each appliance’s characteristics. We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping (DTW) as well as the classification performance of an MLP discriminator as metrics. Additionally, to ensure that the VAs allow to meaningfully train ML models, we use them to generate synthetic data and then train Non intrusive Load Monitoring (NILM) models in an extensive evaluation. The presented evaluation provides evidence that the VA models produce realistic and meaningful results.

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VA-Creator - 基于自适应神经网络生成合成功耗模式的虚拟设备创建器
随着智能家居领域的出现和机器学习(ML)应用的日益广泛,获取能耗数据变得越来越重要。使用传感器收集真实世界的能耗数据耗时长、成本高、容易出错,而且在某些情况下根本无法实现。因此,我们提出了虚拟设备创建器,这是一个创建虚拟设备(VA)的框架。这些虚拟电器基于神经网络(NN)合成功耗模式(PCP),而神经网络的架构则根据训练数据结构进行调整,从而简化了新虚拟电器的创建过程。为了能够生成典型家庭中的所有电器类型,我们使用了各种类型的 NN,包括多层感知器 (MLP)、长短期记忆 (LSTM) 和特定的生成对抗网络 (GAN),以及不同的 ML 技术(如 XGBoost),并根据每种电器的特性选择合适的技术。然后,我们将 ML 模型的结果与真实数据进行比较,并使用动态时间扭曲(DTW)以及 MLP 识别器的分类性能作为指标对其进行评估。此外,为了确保虚拟机构能够有意义地训练 ML 模型,我们使用虚拟机构生成合成数据,然后在广泛的评估中训练非侵入式负载监控(NILM)模型。所提交的评估证明,VA 模型能产生真实而有意义的结果。
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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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