4,739 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
A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings
Despite the increasing capabilities of information technologies for data acquisition and processing,
building energy management systems still require manual configuration and supervision to achieve
optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly
heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic
characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based
on simplified linear models, which support faster computation but also present some limitations regarding
interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel
MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in
non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next
day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured
according to the expected building conditions (weather and occupancy). The system has been implemented
and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding
energy savings around 35% during the intermediate winter season and 20% in the whole winter season with
respect to the current operation of the heating equipment.This work was supported in part by the Universidad de Granada under Grant P9-2014-ING, in part by the Spanish Ministry of Science,
Innovation and Universities under Grant TIN2017-91223-EXP, in part by the Spanish Ministry of Economy and Competitiveness under
Grant TIN2015-64776-C3-1-R, and in part by the European Union (Energy IN TIME EeB.NMP.2013-4), under Grant 608981
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