Abstract
The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.
Highlights
According to the Kyoto protocol of 2008, the electricity used in buildings constitutes 40% of global consumption [1]
The simulations showed that when modelling specific appliances, some artificial neural networks (ANNs) types may not be able to estimate their activity precisely
It is recommended to create hybrid solutions combining different types of ANNs as part of cascading solutions, but primarily as models working in parallel
Summary
According to the Kyoto protocol of 2008, the electricity used in buildings constitutes 40% of global consumption [1]. Households in the European Union are estimated to be responsible for more than 27% of the total energy consumption (in 2017), which makes them the second largest source of demand. Transport exceeds these values and consumes more energy [2]. Knowing the time patterns of energy demand is crucial from the point of view of managing and optimising energy consumption. Optimisation processes are understood as those leading to reduction in electricity consumption and electricity acquisition costs (e.g., in the case of zonal tariffs)
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