50,214 research outputs found
Evaluating prediction models for electricity consumption
This paper presents a system for visualizing electricity consumptiondata along with the implementation of a pattern recognition approach for peakprediction. Various classification algorithms and machine learning techniques aretested and discussed; in particular, Support Vector Machine (SVM), GaussianMixture Model (GMM) and hierarchical classifiers. Most notably, the bestalgorithms are able to detect 70% of the peaks occurring within the next 24 hours.Also, various ways of correlating energy consumption are considered in the presentcontext. Finally, a few directions for future work are discussed
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
Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management
This paper describes the application of stochastic grey-box modeling to
identify electrical power consumption-to-temperature models of a domestic
freezer using experimental measurements. The models are formulated using
stochastic differential equations (SDEs), estimated by maximum likelihood
estimation (MLE), validated through the model residuals analysis and
cross-validated to detect model over-fitting. A nonlinear model based on the
reversed Carnot cycle is also presented and included in the modeling
performance analysis. As an application of the models, we apply model
predictive control (MPC) to shift the electricity consumption of a freezer in
demand response experiments, thereby addressing the model selection problem
also from the application point of view and showing in an experimental context
the ability of MPC to exploit the freezer as a demand side resource (DSR).Comment: Submitted to Sustainable Energy Grids and Networks (SEGAN). Accepted
for publicatio
Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model
Long-term load forecasting plays a vital role for utilities and planners in
terms of grid development and expansion planning. An overestimate of long-term
electricity load will result in substantial wasted investment in the
construction of excess power facilities, while an underestimate of future load
will result in insufficient generation and unmet demand. This paper presents
first-of-its-kind approach to use multiplicative error model (MEM) in
forecasting load for long-term horizon. MEM originates from the structure of
autoregressive conditional heteroscedasticity (ARCH) model where conditional
variance is dynamically parameterized and it multiplicatively interacts with an
innovation term of time-series. Historical load data, accessed from a U.S.
regional transmission operator, and recession data for years 1993-2016 is used
in this study. The superiority of considering volatility is proven by
out-of-sample forecast results as well as directional accuracy during the great
economic recession of 2008. To incorporate future volatility, backtesting of
MEM model is performed. Two performance indicators used to assess the proposed
model are mean absolute percentage error (for both in-sample model fit and
out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table
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