Abstract
Effective electricity management on the consumer side is a key element in optimizing power system operation, particularly given the increasing share of renewable energy sources. Non-intrusive load monitoring (NILM) plays a crucial role by enabling detailed insight into energy consumption patterns. The obtained results enable load profile optimization by eliminating unnecessary consumption and shifting demand to off-peak periods and periods of high renewable generation. This paper presents a NILM system designed for real-world conditions characterized by the simultaneous operation of multiple devices and the presence of signal disturbances. The proposed approach is based on the synergistic use of steady-state and transient features, which serve as inputs to a multimodal deep neural network. The proposed method achieved an accuracy of 93.7%, confirming the effectiveness of multimodal feature fusion.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.81
Multimodal Neural Network with Time Series and Aggregated Feature Fusion for Electricity Consumption Optimization
Effective electricity management on the consumer side is a key element in optimizing power system operation, particularly given the increasing share of renewable energy sources. Non-intrusive load monitoring (NILM) plays a crucial role by enabling detailed insight into energy consumption patterns. The obtained results enable load profile optimization by eliminating unnecessary consumption and shifting demand to off-peak periods and periods of high renewable generation. This paper presents a NILM system designed for real-world conditions characterized by the simultaneous operation of multiple devices and the presence of signal disturbances. The proposed approach is based on the synergistic use of steady-state and transient features, which serve as inputs to a multimodal deep neural network. The proposed method achieved an accuracy of 93.7%, confirming the effectiveness of multimodal feature fusion.
Recommended Citation
Bartman, J., Twarog, B., Kwater, T. & Hawro, P.(2026). Multimodal Neural Network with Time Series and Aggregated Feature Fusion for Electricity Consumption Optimization. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.81