27,734 research outputs found
Aggregation Algorithm Vs. Average for Time Series Prediction
Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times se- ries prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average
Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data
Big spatio-temporal datasets, available through both open and administrative
data sources, offer significant potential for social science research. The
magnitude of the data allows for increased resolution and analysis at
individual level. While there are recent advances in forecasting techniques for
highly granular temporal data, little attention is given to segmenting the time
series and finding homogeneous patterns. In this paper, it is proposed to
estimate behavioral profiles of individuals' activities over time using
Gaussian Process-based models. In particular, the aim is to investigate how
individuals or groups may be clustered according to the model parameters. Such
a Bayesian non-parametric method is then tested by looking at the
predictability of the segments using a combination of models to fit different
parts of the temporal profiles. Model validity is then tested on a set of
holdout data. The dataset consists of half hourly energy consumption records
from smart meters from more than 100,000 households in the UK and covers the
period from 2015 to 2016. The methodological approach developed in the paper
may be easily applied to datasets of similar structure and granularity, for
example social media data, and may lead to improved accuracy in the prediction
of social dynamics and behavior
Neural network ensembles: Evaluation of aggregation algorithms
Ensembles of artificial neural networks show improved generalization
capabilities that outperform those of single networks. However, for aggregation
to be effective, the individual networks must be as accurate and diverse as
possible. An important problem is, then, how to tune the aggregate members in
order to have an optimal compromise between these two conflicting conditions.
We present here an extensive evaluation of several algorithms for ensemble
construction, including new proposals and comparing them with standard methods
in the literature. We also discuss a potential problem with sequential
aggregation algorithms: the non-frequent but damaging selection through their
heuristics of particularly bad ensemble members. We introduce modified
algorithms that cope with this problem by allowing individual weighting of
aggregate members. Our algorithms and their weighted modifications are
favorably tested against other methods in the literature, producing a sensible
improvement in performance on most of the standard statistical databases used
as benchmarks.Comment: 35 pages, 2 figures, In press AI Journa
Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis
We present a new high-resolution global renewable energy atlas ({REatlas})
that can be used to calculate customised hourly time series of wind and solar
PV power generation. In this paper, the atlas is applied to produce
32-year-long hourly model wind power time series for Denmark for each
historical and future year between 1980 and 2035. These are calibrated and
validated against real production data from the period 2000 to 2010. The high
number of years allows us to discuss how the characteristics of Danish wind
power generation varies between individual weather years. As an example, the
annual energy production is found to vary by from the average.
Furthermore, we show how the production pattern change as small onshore
turbines are gradually replaced by large onshore and offshore turbines.
Finally, we compare our wind power time series for 2020 to corresponding data
from a handful of Danish energy system models. The aim is to illustrate how
current differences in model wind may result in significant differences in
technical and economical model predictions. These include up to
differences in installed capacity and differences in system reserve
requirements
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