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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into âmeta-featuresâ to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%â31.6% for Case A and 16.2%â49.4% for Case B), generalization (improvement of 6.7%â29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%â34.1% for Case A and 1.8%â19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
Circulation
Circulation is important to distributions of salt, of deep-ocean heat and hence regional climate, of pollutants and of many species carried by the flow during their lifecycle. Currents affect offshore operations and habitats.
Five sections from 1957 to 2004 suggest decline of the Atlantic Meridional Overturning Circulatin (AMOC) but this is within the range of large variability on time-scales of weeks to months. An overall trend has not been determined from the continuous measurements begun in 2004.
Deep outflows of cold water from the Nordic seas are likewise too variable to infer any overall trend.
Strong North Atlantic flow eastwards towards the UK may correlate with positive North Atlantic Oscillation (NAO) Index (i.e. prevailing westerly winds). Enhanced along-slope current around the UK may correlate with a negative NAO Index.
Climate modelsâ consensus makes it very likely that AMOC will decrease over the next century, but not âshut downâ completely.
Similar spatial and temporal variability (arising from complex topography and variable forcing) is likely in future
An Unsupervised Deep Learning Approach for Scenario Forecasts
In this paper, we propose a novel scenario forecasts approach which can be
applied to a broad range of power system operations (e.g., wind, solar, load)
over various forecasts horizons and prediction intervals. This approach is
model-free and data-driven, producing a set of scenarios that represent
possible future behaviors based only on historical observations and point
forecasts. It first applies a newly-developed unsupervised deep learning
framework, the generative adversarial networks, to learn the intrinsic patterns
in historical renewable generation data. Then by solving an optimization
problem, we are able to quickly generate large number of realistic future
scenarios. The proposed method has been applied to a wind power generation and
forecasting dataset from national renewable energy laboratory. Simulation
results indicate our method is able to generate scenarios that capture spatial
and temporal correlations. Our code and simulation datasets are freely
available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available
at https://github.com/chennnnnyize/Scenario-Forecasts-GA
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