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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.