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With the rise of the Smart Home sector and the growing adoption of Machine Learning (ML), the acquisition of power consumption data is becoming increasingly significant. Gathering real-world energy measurements using sensors is often time-intensive, costly, error-prone, and, in some cases, entirely infeasible. To address this, we introduce the VA-Creator, a framework designed for generating Virtual Appliances (VAs). These VAs produce synthetic power consumption patterns (PCPs) using Neural Networks (NNs) that dynamically adjust their architecture according to the structure of the training data, thereby facilitating the development of additional VAs. To cover every appliance category commonly found in a typical household, we employ a range of neural network types—including Multilayer Perceptrons (MLPs), Long Short-Term Memory networks (LSTMs), and a tailored Generative Adversarial Network (GAN)—alongside other ML approaches such as XGBoost, choosing the most suitable method based on the specific properties of each appliance. The outputs of these ML models are subsequently compared with actual measurements and evaluated using Dynamic Time Warping (DTW) and MLP-based discriminator classification accuracy as evaluation metrics. Furthermore, to verify that the VAs enable effective training of ML models, we generate synthetic datasets with them and conduct an in-depth evaluation by training Non-Intrusive Load Monitoring (NILM) models. The results of this comprehensive assessment confirm that the VA models yield realistic and practically useful outputs.

The field of smart homes stands out as one of the most dynamic domains in advancing information and communication technologies. To deliver enhanced safety, user convenience, and efficient resource use, a smart home should be regarded as an advanced technological setup that integrates automation innovations with up-to-date building practices. Such systems can be incorporated into both newly constructed structures and the upgrading of older ones. This research explores the core concept of a smart home system, justifies the adoption of resource-efficient approaches and solutions, and assesses how these can be combined with the renovation of low-rise housing. The work presents a conceptual design of a smart home configuration tailored to a specific renovation case, along with a mobile-based interface for system management.