182 research outputs found
Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model. ESRI WP585, February 2018
Power systems based on renewable energy sources (RES) are characterised by
increasingly distributed, volatile and uncertain supply leading to growing requirements for
flexibility. In this paper, we explore the role of demand response (DR) as a source of flexibility
that is considered to become increasingly important in future. The majority of research in this
context has focussed on the operation of power systems in energy only markets, mostly using
deterministic optimisation models. In contrast, we explore the impact of DR on generator
investments and profits from different markets, on costs for different consumers from
different markets, and on CO2 emissions under consideration of the uncertainties associated
with the RES generation. We also analyse the effect of the presence of a feed-in premium
(FIP) for RES generation on these impacts. We therefore develop a novel stochastic mixed
complementarity model in this paper that considers both operational and investment
decisions, that considers interactions between an energy market, a capacity market and a
feed-in premium and that takes into account the stochasticity of electricity generation by RES.
We use a Benders decomposition algorithm to reduce the computational expenses of the
model and apply the model to a case study based on the future Irish power system. We find
that DR particularly increases renewable generator profits. While DR may reduce consumer
costs from the energy market, these savings may be (over)compensated by increasing costs
from the capacity market and the feed-in premium. This result highlights the importance of
considering such interactions between different markets
An Information Theoretic Approach to Quantify the Stability of Feature Selection and Ranking Algorithms
[EN] Feature selection is a key step when dealing with high-dimensional data. In particular, these techniques simplify the process of knowledge discovery from the data in fields like biomedicine, bioinformatics, genetics or chemometrics by selecting the most relevant features out of the noisy, redundant and irrel- evant features. A problem that arises in many of these applications is that the outcome of the feature selection algorithm is not stable. Thus, small variations in the data may yield very different feature rankings. Assessing the stability of these methods becomes an important issue in the previously mentioned situations, but it has been long overlooked in the literature. We propose an information-theoretic approach based on the Jensen-Shannon di-vergence to quantify this robustness. Unlike other stability measures, this metric is suitable for different algorithm outcomes: full ranked lists, top-k lists (feature subsets) as well as the lesser studied partial ranked lists that keep the k best ranked elements. This generalized metric quantifies the dif-ference among a whole set of lists with the same size, following a probabilistic approach and being able to give more importance to the disagreements that appear at the top of the list. Moreover, it possesses desirable properties for a stability metric including correction for change, and upper/lower bounds and conditions for a deterministic selection. We illustrate the use of this stability metric with data generated in a fully controlled way and compare it with popular metrics including the Spearman’s rank correlation and the Kuncheva’s index on feature ranking and selection outcomes respectively.S
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes
The Bayesian additive regression trees (BART) model is an ensemble method
extensively and successfully used in regression tasks due to its consistently
strong predictive performance and its ability to quantify uncertainty. BART
combines "weak" tree models through a set of shrinkage priors, whereby each
tree explains a small portion of the variability in the data. However, the lack
of smoothness and the absence of a covariance structure over the observations
in standard BART can yield poor performance in cases where such assumptions
would be necessary. We propose Gaussian processes Bayesian additive regression
trees (GP-BART) as an extension of BART which assumes Gaussian process (GP)
priors for the predictions of each terminal node among all trees. We illustrate
our model on simulated and real data and compare its performance to traditional
modelling approaches, outperforming them in many scenarios. An implementation
of our method is available in the R package rGPBART available at:
https://github.com/MateusMaiaDS/gpbar
Effect of the Irish Civil War 1922-1923 on suicide rates in Ireland: a retrospective investigation of the archives of the registrar-general for Saorstát Éireann
Introduction: Emile Durkheim differentiated between two types of wars: National and Civil Wars in terms of effect on suicide mortality. This study investigates Durkheim’s assertion by examining the effect of Irish Civil War on the 1882-1928 suicide rates trend.
Method: We used Auto-Regressive Integrated Moving Average with Explanatory variables (ARIMAX) design adopting Bayesian approach.
Results: The odds for death by suicide for the total Irish population during the civil war period were calculated as 0.932 (95% CI: 0.753 to 1.125). This translates to a reduction in the suicide rates by 6.7% (95% CI: 24.7% to -12.5%). The odds for death by suicide for the total Irish population during the First World War period were calculated as 0.872 (95% CI: 0.754 to 0.997). This indicates to a reduction in the suicide rates by 12.8% (95% CI: 24.6% to 0.3%).
Conclusion: Evidence from this study support a significant drop in terms of the intensity of suicidal behaviour in the Irish population during World War I more so than during the Irish Civil War.
Conflict of interest: non
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Change points of global temperature
We aim to address the question of whether or not there is a significant recent 'hiatus', 'pause' or 'slowdown' of global temperature rise. Using a statistical technique known as change point (CP) analysis we identify the changes in four global temperature records and estimate the rates of temperature rise before and after these changes occur. For each record the results indicate that three CPs are enough to accurately capture the variability in the data with no evidence of any detectable change in the global warming trend since ∼1970. We conclude that the term 'hiatus' or 'pause' cannot be statistically justified
Bayesian Additive Regression Trees with Model Trees
Bayesian Additive Regression Trees (BART) is a tree-based machine learning
method that has been successfully applied to regression and classification
problems. BART assumes regularisation priors on a set of trees that work as
weak learners and is very flexible for predicting in the presence of
non-linearity and high-order interactions. In this paper, we introduce an
extension of BART, called Model Trees BART (MOTR-BART), that considers
piecewise linear functions at node levels instead of piecewise constants. In
MOTR-BART, rather than having a unique value at node level for the prediction,
a linear predictor is estimated considering the covariates that have been used
as the split variables in the corresponding tree. In our approach, local
linearities are captured more efficiently and fewer trees are required to
achieve equal or better performance than BART. Via simulation studies and real
data applications, we compare MOTR-BART to its main competitors. R code for
MOTR-BART implementation is available at https://github.com/ebprado/MOTR-BART
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