51,404 research outputs found
Holistic Measures for Evaluating Prediction Models in Smart Grids
The performance of prediction models is often based on "abstract metrics"
that estimate the model's ability to limit residual errors between the observed
and predicted values. However, meaningful evaluation and selection of
prediction models for end-user domains requires holistic and
application-sensitive performance measures. Inspired by energy consumption
prediction models used in the emerging "big data" domain of Smart Power Grids,
we propose a suite of performance measures to rationally compare models along
the dimensions of scale independence, reliability, volatility and cost. We
include both application independent and dependent measures, the latter
parameterized to allow customization by domain experts to fit their scenario.
While our measures are generalizable to other domains, we offer an empirical
analysis using real energy use data for three Smart Grid applications:
planning, customer education and demand response, which are relevant for energy
sustainability. Our results underscore the value of the proposed measures to
offer a deeper insight into models' behavior and their impact on real
applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on
Knowledge and Data Engineering, 2014. Authors' final version. Copyright
transferred to IEE
Autoregressive Time Series Forecasting of Computational Demand
We study the predictive power of autoregressive moving average models when
forecasting demand in two shared computational networks, PlanetLab and Tycoon.
Demand in these networks is very volatile, and predictive techniques to plan
usage in advance can improve the performance obtained drastically.
Our key finding is that a random walk predictor performs best for
one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential
smoothing models perform better for two and three-step-ahead forecasts. A Monte
Carlo bootstrap test is proposed to evaluate the continuous prediction
performance of different models with arbitrary confidence and statistical
significance levels. Although the prediction results differ between the Tycoon
and PlanetLab networks, we observe very similar overall statistical properties,
such as volatility dynamics
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