Abstract
Increased levels of electrification of home appliances, heating and transportation are bringing new challenges for the smart grid, as energy supply sources need to be managed more efficiently. In order to minimize production costs, reduce the impact on the environment, and optimize electricity pricing, producers need to be able to accurately estimate their customers’ demand. As a result, forecasting electricity usage plays an important role in smart grids since it enables matching supply with demand, and thus minimize energy waste. Forecasting is becoming increasingly important in very small-scale power networks, also known as microgrids, as these systems should be able to operate autonomously, in islanded mode. The aim of this paper is to evaluate the efficiency of several forecasting methods in such very small networks. We evaluate artificial neural networks (ANN), wavelet neural networks (WNN), auto-regressive moving-average (ARMA), multi-regression (MR) and auto-regressive multi-regression (ARMR) on an aggregate of 30 houses, which emulates the demand of a rural isolated microgrid. Finally, we empirically show that for this problem ANN is the most efficient technique for predicting the following day’s demand.
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Notes
- 1.
See Fig. 14 on page xx: http://www.eia.gov.
- 2.
BRIC: Brazil, Russia, India and China.
- 3.
Source: http://www.eia.gov and http://data.worldbank.org/.
- 4.
Source: https://code.google.com/p/fanntool/.
- 5.
ARMA-NWM: ARMA No Window-Time Moving.
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This work was supported by Trinity College Dublin.
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A Appendix
A Appendix
See the Figs. 14, 15, 16, 17 and 18.
Evolution of world electrical consumption by continent from 1980 to 2012
Used process to build a WNN
Average of total NRMSE for WNN - previous day
Average of total NRMSE for WNN - previous week
Comparison of Hourly NRMSE along one year
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Cavallo, J., Marinescu, A., Dusparic, I., Clarke, S. (2015). Evaluation of Forecasting Methods for Very Small-Scale Networks. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2015. Lecture Notes in Computer Science(), vol 9518. Springer, Cham. https://doi.org/10.1007/978-3-319-27430-0_5
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