Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
Discerning Electricity Consumption Patterns from Urban Allometric Scaling
COMPENG '10 Proceedings of the 2010 Complexity in Engineering
Short-term load forecasting using lifting scheme and ARIMA models
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An early warning system against malicious activities for smart grid communications
IEEE Network: The Magazine of Global Internetworking
A prediction model for anomalies in smart grid with sensor network
Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop
Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
Proceedings of the 12th international conference on Information processing in sensor networks
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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.