Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings

This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown.Comment: 10 pages, 13 figures, 3 table

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

This paper presents a new algorithm to extract device profiles fully unsupervised from three phases reactive and active aggregate power measurements. The extracted device profiles are applied for the disaggregation of the aggregate power measurements using particle swarm optimization. Finally, this paper provides a new approach for short term power predictions using the disaggregation data. For this purpose, a state changes forecast for every device is carried out by an artificial neural network and converted into a power prediction afterwards by reconstructing the power regarding the state changes and the device profiles. The forecast horizon is 15 minutes. To demonstrate the developed approaches, three phase reactive and active aggregate power measurements of a multi-tenant commercial building are used. The granularity of data is 1 s. In this work, 52 device profiles are extracted from the aggregate power data. The disaggregation shows a very accurate reconstruction of the measured power with a percentage energy error of approximately 1 %. The developed indirect power prediction method applied to the measured power data outperforms two persistence forecasts and an artificial neural network, which is designed for 24h-day-ahead power predictions working in the power domain.Comment: 15 pages, 14 figures, 4 table

Multifunctional metal oxide electrodes: Colour for thin film solar cells

Building integrated photovoltaics (BIPV) is an essential part to reduce the CO2 footprint of metropolitan areas. Currently, full integration of photovoltaic elements in a building is a very costly and complex undertaking, as it usually requires expensive custom modules. In order to increase the market share of BIPV in the residential mass market, a low-cost, flexible technical process to change the appearance of solar elements is required. Transparent conductive electrodes consisting of an oxide-metal-oxide (OMO) stack of thin layers have been optimized for application in thin film solar cells. Here the OMO stack is multifunctional: It provides the transparent front contact electrode and at the same time allows tuning of the module colour. This has several advantages compared to other colouring techniques, i.e. coloured glass or additional interlayers. The OMO colouring does not require an additional process step, and with sputtering an already existing deposition method is used. By varying the thickness of the oxide layers it is possible to change the reflected spectrum of the stack and with it the module colour. In this publication, we present how the optical model of the OMO stack, that is necessary for precise tuning of the colour, is first developed for OMO/glass samples and then report the changes necessary to adapt the OMOs for use on Cu(In,Ga)Se2 thin film solar cells

Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings

This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown