no code implementations • 14 May 2023 • Mina Razghandi, Hao Zhou, Melike Erol-Kantarci, Damla Turgut
In this paper, we propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes.
no code implementations • 19 Jan 2022 • Mina Razghandi, Hao Zhou, Melike Erol-Kantarci, Damla Turgut
To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase. We compared the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model.
no code implementations • 25 Sep 2021 • Mina Razghandi, Hao Zhou, Melike Erol-Kantarci, Damla Turgut
A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns.
no code implementations • 26 Jun 2021 • Mina Razghandi, Hao Zhou, Melike Erol-Kantarci, Damla Turgut
Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities.