NDLI: A dynamic forecasting method for small scale residential electrical demand

Content Provider	IEEE Xplore Digital Library
Author	Marinescu, A. Dusparic, I. Harris, C. Cahill, V. Clarke, S.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Stat., Trinity Coll. Dublin, Dublin, Ireland (Marinescu, A.; Dusparic, I.; Harris, C.; Cahill, V.; Clarke, S.)
Abstract	Small scale electrical demand forecasting is an emerging field motivated by the penetration of renewable energy sources and the growth of microgrids and virtual power plants. These advances pose more complex forecasting challenges compared to the already established large scale forecasting approaches. Current short term load forecasting methods deal with two types of day, normal and anomalous, which are predicted separately. Anomalous days are classified as such ahead of time, based on key calendar events such as public holidays. However, there are some anomalous days which are not always predictable on a day ahead basis. Due to unforeseen events, a seemingly normal day can progress towards an anomalous case causing high errors in prediction. We propose a new dynamic forecasting mechanism that actively monitors residential electrical demand along a forecasted day, and detects anomalous pattern changes from a previously predicted demand of the day. A self-organising map is employed to detect anomalous days as they progress. Once an anomaly is detected, a neural network based prediction system changes its input neurons according to a previously detected and recorded match found in a database of anomalous days, in order to accommodate the anomalous day prediction. Results are based on measured power demands recorded in Ireland from domestic smart-meters between 2009-2011, and focus on small scale residential electrical demands of up to 350 kWh. During anomalous days our dynamic prediction approach achieves forecasting results within 3.63% of the real load, down from the 7.37% obtained by the initial prediction algorithm and the 5.41% achieved by standalone re-prediction, without pattern matching.
Starting Page	3767
Ending Page	3774
File Size	3682328
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Forecasting Accuracy Neurons Load forecasting Shape Microgrids
Content Type	Text
Resource Type	Article

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