6,830 research outputs found
Inefficiencies in Digital Advertising Markets
Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research
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A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness
Time-based pricing programs for domestic electricity users have been effective in reducing peak demand and facilitating renewables integration. Nevertheless, high cost, price non-responsiveness and adverse selection may create the possible challenges. To overcome these challenges, it can be fruitful to investigate the ‘high-potential’ users, which are more responsive to price changes and apply time-based pricing to these users. Few studies have investigated how to identify which users are more price-responsive. We aim to fill this gap by comprehensively identifying the drivers of domestic users’ price responsiveness, in order to facilitate the selection of the high-potential users. We adopt a novel data-driven approach, first by a feed forward neural network model to accurately determine the baseline monthly peak consumption of individual households, followed by an integrated machine-learning variable selection methodology to identify the drivers of price responsiveness applied to Irish smart meter data from 2009-10 as part of a national Time of Use trial. This methodology substantially outperforms traditional variable selection methods by combining three advanced machine-learning techniques. Our results show that the response of energy users to price change is affected by a number of factors, ranging from demographic and dwelling characteristics, psychological factors, historical electricity consumption, to appliance ownership. In particular, historical electricity consumption, income, the number of occupants, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are found to be significant drivers of price responsiveness. We also observe that continual price increase within a moderate range does not drive additional peak demand reduction, and that there is an intention-behaviour gap, whereby stated intention does not lead to actual peak reduction behavior. Based on our findings, we have conducted scenario analysis to demonstrate the feasibility of selecting the high potential users to achieve significant peak reduction
Stability Analysis of Wholesale Electricity Markets under Dynamic Consumption Models and Real-Time Pricing
This paper analyzes stability conditions for wholesale electricity markets
under real-time retail pricing and realistic consumption models with memory,
which explicitly take into account previous electricity prices and consumption
levels. By passing on the current retail price of electricity from supplier to
consumer and feeding the observed consumption back to the supplier, a
closed-loop dynamical system for electricity prices and consumption arises
whose stability is to be investigated. Under mild assumptions on the generation
cost of electricity and consumers' backlog disutility functions, we show that,
for consumer models with price memory only, market stability is achieved if the
ratio between the consumers' marginal backlog disutility and the suppliers'
marginal cost of supply remains below a fixed threshold. Further, consumer
models with price and consumption memory can result in greater stability
regions and faster convergence to the equilibrium compared to models with price
memory alone, if consumption deviations from nominal demand are adequately
penalized.Comment: 8 pages, 7 Figures, accepted to the 2017 American Control Conferenc
A Primer on Causality in Data Science
Many questions in Data Science are fundamentally causal in that our objective
is to learn the effect of some exposure, randomized or not, on an outcome
interest. Even studies that are seemingly non-causal, such as those with the
goal of prediction or prevalence estimation, have causal elements, including
differential censoring or measurement. As a result, we, as Data Scientists,
need to consider the underlying causal mechanisms that gave rise to the data,
rather than simply the pattern or association observed in those data. In this
work, we review the 'Causal Roadmap' of Petersen and van der Laan (2014) to
provide an introduction to some key concepts in causal inference. Similar to
other causal frameworks, the steps of the Roadmap include clearly stating the
scientific question, defining of the causal model, translating the scientific
question into a causal parameter, assessing the assumptions needed to express
the causal parameter as a statistical estimand, implementation of statistical
estimators including parametric and semi-parametric methods, and interpretation
of our findings. We believe that using such a framework in Data Science will
help to ensure that our statistical analyses are guided by the scientific
question driving our research, while avoiding over-interpreting our results. We
focus on the effect of an exposure occurring at a single time point and
highlight the use of targeted maximum likelihood estimation (TMLE) with Super
Learner.Comment: 26 pages (with references); 4 figure
Targeted demand response for flexible energy communities using clustering techniques
The present study proposes clustering techniques for designing demand
response (DR) programs for commercial and residential prosumers. The goal is to
alter the consumption behavior of the prosumers within a distributed energy
community in Italy. This aggregation aims to: a) minimize the reverse power
flow at the primary substation, occuring when generation from solar panels in
the local grid exceeds consumption, and b) shift the system wide peak demand,
that typically occurs during late afternoon. Regarding the clustering stage, we
consider daily prosumer load profiles and divide them across the extracted
clusters. Three popular machine learning algorithms are employed, namely
k-means, k-medoids and agglomerative clustering. We evaluate the methods using
multiple metrics including a novel metric proposed within this study, namely
peak performance score (PPS). The k-means algorithm with dynamic time warping
distance considering 14 clusters exhibits the highest performance with a PPS of
0.689. Subsequently, we analyze each extracted cluster with respect to load
shape, entropy, and load types. These characteristics are used to distinguish
the clusters that have the potential to serve the optimization objectives by
matching them to proper DR schemes including time of use, critical peak
pricing, and real-time pricing. Our results confirm the effectiveness of the
proposed clustering algorithm in generating meaningful flexibility clusters,
while the derived DR pricing policy encourages consumption during off-peak
hours. The developed methodology is robust to the low availability and quality
of training datasets and can be used by aggregator companies for segmenting
energy communities and developing personalized DR policies
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