Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks

Abstract

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.

From home energy management systems to energy communities: methods and data

“…The approach described above is applied to PV power generation, with great success. Moving from deterministic forecasting to probability forecasting, for both load demand and PV power generation 58 Test and validate different non-invasive load monitoring (NILM) algorithms, as performed in 59 , 60 . The first reference employs ApproxHull 61 , a data selection tool existing in our lab to deep learning models.…”

“…Test and validate different non-invasive load monitoring (NILM) algorithms, as performed in 59 , 60 . The first reference employs ApproxHull 61 , a data selection tool existing in our lab to deep learning models.…”