Pervasive Data Analytics for Sustainable Energy Systems

With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates efforts to balance supply and demand, since their generation is intermittent and unpredictable. Traditionally, it has always been the supply side that has adapted to follow energy demand, however, in order to have a sustainable energy system for the future, the demand side will have to be better managed to match the available energy supply. In the first part of this thesis, we focus on understanding customers' energy consumption behavior (demand analytics). While previously, information about customer's energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we then develop a versatile customer segmentation framework, track cluster changes over time, and identify key characteristics that define a cluster. Additionally, although household-level consumption is hard to predict, it can be used to improve aggregate-level forecasting by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the customer base. Furthermore, we develop an approach to model the uncertainty of future demand. In contrast to previous work that used computationally expensive methods, such as simulation, bootstrapping, or ensemble, we construct prediction intervals directly using the time-varying conditional mean and variance of future demand. While analytics on customer energy data are indeed essential to understanding customer behavior, they could also lead to breaches of privacy, with all the attendant risks. The first part of this thesis closes by exploring symbolic representations of smart meter data which still allow learning algorithms to be performed on top of them, thus providing a trade-off between accurate analytics and the protection of customer privacy. In the second part of this thesis, we focus on mechanisms for incentivizing changes in customers' energy usage in order to maintain (electricity) grid stability, i.e., Demand Response (DR). We complement previous work in this area (which typically targeted large, industrial customers) by studying the application of DR to residential customers. We first study the influence of DR baselines, i.e., estimates of what customers would have consumed in the absence of a DR event. While the literature to date has focused on baseline accuracy and bias, we go beyond these concepts by explaining how a baseline affects customer participation in a DR event, and how it affects both the customer and company profit. We then discuss a strategy for matching the demand side with the supply side by using a multiunit auction performed by intelligent agents on behalf of customers. The thesis closes by eliciting behavioral incentives from the crowd of customers for promoting and maintaining customer engagement in DR programs

EFFECT OF CHEWING POLYOLS GUMS ON THE ACCUMULATION OF DENTAL PLAQUE AROUND ORTHODONTIC BRACKETS: EFEK MENGUNYAH PERMEN KARET POLYOL TERHADAP AKUMULASI PLAK DI SEKITAR PERLEKATAN BRAKET

This study aimed to investigate the effect of xylitol compared to sorbitol as polyols chewing gum on the plaque accumulation during treatment with fixed orthodontic appliances. This clinical trial comprised 20 healthy orthodontic patients aged 18 –to-30-years old. The subjects were randomly assigned into 2 groups: xylitol group (n=10) and sorbitol group (n-10). The subjects were instructed to chew 2 pieces of tested chewing gum three times everyday for a 14-days period of experiment. The xylitol gum contained of 1300 mg xylitol per serving (2 pieces of gum) while the sorbitol gum was sweetened with sorbitol. At baseline and after 14 days the plaque were collected from bucco-cervical surfaces around brackets of premolar and front teeth and then dried weigh. The result of this study showed that the means of plaque weight in the xylitol group at baseline and after 14 days were 2.75 mg ± 2.59 mg and 0.92 mg ± 1.23 mg respectively, while the means of plaque weight in the sorbitol group at baseline and after 14 days were: 2.13 mg ± 0.94 mg and 2.37 mg ± 0.89 mg respectively. After one way ANOVA and post hoc test, there was no difference between plaque weight in both groups at baseline (p=0.2) while the plaque weight in the xylitol group was significantly lower than the sorbitol group after 14 days (p < 0.01). In conclusion, 14-days use of xylitol- containing chewing gums, corresponding to a daily amount of 4 grams of xylitol, could eliminate the plaque accumulation in fixed orthodontic patients

MM-GEF: Multi-modal representation meet collaborative filtering

In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods

Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors

The proliferation of DeepFake technology is a rising challenge in today's society, owing to more powerful and accessible generation methods. To counter this, the research community has developed detectors of ever-increasing accuracy. However, the ability to explain the decisions of such models to users is lacking behind and is considered an accessory in large-scale benchmarks, despite being a crucial requirement for the correct deployment of automated tools for content moderation. We attribute the issue to the reliance on qualitative comparisons and the lack of established metrics. We describe a simple set of metrics to evaluate the visual quality and informativeness of explanations of video DeepFake classifiers from a human-centric perspective. With these metrics, we compare common approaches to improve explanation quality and discuss their effect on both classification and explanation performance on the recent DFDC and DFD datasets.Comment: Accepted at BMVC 2022, code repository at https://github.com/baldassarreFe/deepfake-detectio

Online Unsupervised State Recognition in Sensor Data

Smart sensors, such as smart meters or smart phones, are nowadays ubiquitous. To be "smart", however, they need to process their input data with limited storage and computational resources. In this paper, we convert the stream of sensor data into a stream of symbols, and further, to higher level symbols in such a way that common analytical tasks such as anomaly detection, forecasting or state recognition, can still be carried out on the transformed data with almost no loss of accuracy, and using far fewer resources. We identify states of a monitored system and convert them into symbols (thus, reducing data size), while keeping "interesting" events, such as anomalies or transition between states, as it is. Our algorithm is able to find states of various length in an online and unsupervised way, which is crucial since behavior of the system is not known beforehand. We show the effectiveness of our approach using real-world datasets and various application scenarios

Forecasting Uncertainty in Electricity Demand

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and leptokurtic caused by the nature of energy data. In this paper we propose a novel approach to estimate the time-varying conditional variance of the GAM residuals, which we call the GAM^2 algorithm. It allows utility companies and network operators to assess the uncertainty of future electricity demand and incorporate it into their planning processes. The basic idea of our algorithm is to apply another GAM to the squared residuals to explain the dependence of uncertainty on exogenous variables. Empirical evidence shows that the residuals rescaled by the estimated conditional variance are approximately normal. We combine our modeling approach with online learning algorithms that adjust for dynamic changes in the distributions of demand. We illustrate our method by a case study on data from RTE, the operator of the French transmission grid

When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies have investigated baseline accuracy and bias for large, industrial and commercial customers. However, the analysis of baseline performance for residential customers has received less attention. In this paper, we analyze DR baselines for residential customers. Our analysis goes beyond accuracy and bias by understanding the impact of baselines on all stakeholders’ profit. Using our customer models, we successfully show how customer participation changes depending on the incentive actually received. We found that, in general, bias is more relevant than accuracy for determining which baseline provides the highest profit to stakeholders. Consequently, this result provides a valuable insight into designing effective DR incentive schemes

SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels

Nowadays, the use of intelligent systems to detect redundant information in news articles has become especially prevalent with the proliferation of news media outlets in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious findings in these systems: Simple heuristics such as whether a pair of news are both about politics can provide strong but deceptive downstream performance. Segmenting news similarity datasets into topics improves the training of these models by forcing them to learn how to distinguish salient characteristics under more narrow domains. However, this requires the existence of topic-specific datasets, which are currently lacking. In this article, we propose a new dataset of similar news, SPICED, which includes seven topics: Crime & Law, Culture & Entertainment, Disasters & Accidents, Economy & Business, Politics & Conflicts, Science & Technology, and Sports. Futhermore, we present four distinct approaches for generating news pairs, which are used in the creation of datasets specifically designed for news similarity detection task. We benchmarked the created datasets using MinHash, BERT, SBERT, and SimCSE models

FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems

Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending private data to a central server as in traditional approaches, FL decentralizes computations: devices train locally and share updates with a global server. A primary challenge in this setting is achieving fast and accurate model training - vital for recommendation systems where delays can compromise user engagement. This paper introduces FedFNN, an algorithm that accelerates decentralized model training. In FL, only a subset of users are involved in each training epoch. FedFNN employs supervised learning to predict weight updates from unsampled users, using updates from the sampled set. Our evaluations, using real and synthetic data, show: 1. FedFNN achieves training speeds 5x faster than leading methods, maintaining or improving accuracy; 2. the algorithm's performance is consistent regardless of client cluster variations; 3. FedFNN outperforms other methods in scenarios with limited client availability, converging more quickly

Estimating human interactions with electrical appliances for activity-based energy savings recommendations: poster abstract

Since the power consumption of different electrical appliances in a household can be recorded by individual smart meters, it becomes possible to start considering in more details the interactions of the residents with those devices throughout the day. Appliances usages should not be considered as independent events, but rather as enablers for activities. In this work, we propose an automated method for determining when an electrical device is triggered solely from its power trace. Knowing when an appliance is powered on is required for identifying recurrent patterns that could later be understood as activities. Leveraging activity knowledge over time will allow us to design personalized energy efficient measures. We envision the design of future ambient intelligence systems, where the smart home can optimize the energy consumption in regards to the lifestyles of its residents