NDLI: A framework for short-term activity-aware load forecasting

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A framework for short-term activity-aware load forecasting

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A framework for short-term activity-aware load forecasting

Content Provider	ACM Digital Library
Author	Neumann, Martin Alexander Beigl, Michael Ding, Yong Da Silva, Per Goncalves
Abstract	In this paper, we present a framework for implementing short-term load forecasting, in which statistical time series prediction methods and machine learning-based regression methods, can be configured to benchmark their performance against each other on given data of smart meters and other related exogenous variables. Besides the prediction methods, forecasting performance also depends on the quality of training data. This is addressed by two characteristics of our framework on data collection and preprocessing. The first one is to introduce a human activity variable as an additional load influencing factor which reflects anomalous load patterns by aperiodic human activity. The second characteristic is to wavelet transform training data during the preprocessing stage to better extract redundant information from meter data. To investigate the feasibility of the proposed framework, a preliminary case study for predicting daily power consumption of several individual smart meters, using real-world data, is presented. The results indicate that, in general, the aggregation level of meter data and activity data matters.
Starting Page	23
Ending Page	28
Page Count	6
File Format	PDF
ISBN	9781450323468
DOI	10.1145/2516911.2516919
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data preprocessing Machine learning Short-term load forecasting
Content Type	Text
Resource Type	Article

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