Residential Load Modeling With Generative Adversarial Networks

Precise residential load modeling is indispensable for crafting effective demand-side management strategies and simulating realistic household power consumption under diverse conditions. This paper introduces a novel generative framework, leveraging the power of Generative Adversarial Networks (GANs), to synthesize highly realistic daily activity patterns. By training on detailed Italian time-use data, the model captures nuanced behavioral statistics, reflecting the inherent variability of human routines. Furthermore, incorporating conditional generation based on the day of the week allows for contextually rich and adaptable simulations, capturing weekly lifestyle variations. Household power profiles are reconstructed by meticulously mapping the generated activities to the characteristic power signatures of common household appliances, resulting in simulations that exhibit strong concordance with empirical load data at both granular, appliance-level, and aggregated household levels. Critically, our GAN-based approach demonstrably accelerates simulation throughput compared to conventional Markov chain methodologies, enabling the efficient and scalable analysis of complex residential energy scenarios, and opening avenues for real-time applications and large-scale urban energy studies.