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|Title: ||Forecasting hot water consumption in residential houses|
|Authors: ||Gelažanskas, Linas|
Gamage, Kelum A.A.
|Keywords: ||hot water consumption|
|Issue Date: ||2015|
|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.|
|Appears in Collections:||Moksliniai straipsniai / Research articles|
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