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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3746

Title: Forecasting hot water consumption in residential houses
Authors: Gelažanskas, Linas
Gamage, Kelum A.A.
Keywords: hot water consumption
forecasting techniques
smart grid
demand-side management
Issue Date: 2015
Publisher: MDPI
Citation: Gelažanskas,L.; Gamage, Kelum A. A. 2015. Forecasting hot water consumption in residential houses, MDPI 8(11): 12702-12717
Series/Report no.: 8;11
Abstract: An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one of the most energy-intensive devices, where its energy demand is shiftable in time without influencing the comfort level. Tailored hot water usage predictions and advanced control techniques could enable these devices to supply ancillary energy balancing services. The paper analyses a set of hot water consumption data from residential dwellings. This work is an important foundation for the development of a demand-side management strategy based on hot water consumption forecasting at the level of individual residential houses. Various forecasting models, such as exponential smoothing, seasonal autoregressive integrated moving average, seasonal decomposition and a combination of them, are fitted to test different prediction techniques. These models outperform the chosen benchmark models (mean, naive and seasonal naive) and show better performance measure values. The results suggest that seasonal decomposition of the time series plays the most significant part in the accuracy of forecasting.
URI: http://dspace.vgtu.lt/handle/1/3746
ISSN: 1996-1073
Appears in Collections:Moksliniai straipsniai / Research articles

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