Abstract
Smart grids enable large-scale integration of low-carbon energy sources and energy efficiency. However, changing customer energy consumption behavior has been a challenge, requiring the development of services that change the way customers relate with energy to increase energy efficiency and savings in households. To this end, this qualitative study in the Portuguese energy market offers a nuanced understanding of how customer cocreate value with smart energy services, identifying three different customer value cocreation practice styles and respective engagement behaviors). Study findings reveal that while AHEM (Advanced Home Energy Management) and MEM (Mobility Energy Management) customers are willing to play autonomous roles in managing the energy consumption and production, HFEC (Hassle Free Home Energy Consumption) customers may be open to adopt smart energy services without spending time and effort in using these services. The study offers relevant implications for policy makers and ESCOs (energy service companies). Although much attention has been paid to advanced customers, a nuanced approach may enable ESCOs to reach disengaged customers, by offering tailored services that are suited to their hassle free value cocreation practice styles. Policy makers may also explore tailored, and service focused incentives to push the adoption of smart service solutions in large-scale.

Abstract
Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

Abstract
Food waste reduction represents a potential opportunity to enhance environmental sustainability. This is especially important in fresh products such as fresh seafood, where waste levels are substantially higher than those of other food products. In this particular case, reducing waste is also vital to meet demand while conserving fisheries. This paper aims to promote more sustainable supply chains by proposing daily fresh fish demand forecasting models that can be used by grocery retailers to align supply and demand, and hence prevent the production of food waste. To accomplish this goal, we explored the potential of different machine learning models, namely Long Short-Term Memory networks, Feedforward neural networks, Support Vector Regression, and Random Forests, as well as a Holt-Winters statistical model. Demand censorship was considered to capture real demand. To validate the proposed methodology, we estimated the demand for fresh fish in a representative store of a large European retailing company used as a case study. The results revealed that the machine learning models provided accurate forecasts in comparison to the baseline models and the statistical model, with the Long Short-Term Memory networks model yielding, in general, the best results in terms of root mean squared error (27.82), mean absolute error (20.63) and mean positive error (17.86). Thus, the implementation of these types of models can thus have a positive impact on the sustainability of fresh fish species and customer satisfaction.

Abstract
This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE target will likely be achieved (efficacy) and to what degree (effectiveness), depending on the different criteria, including geographical context, characterizing concerns, and policy characteristics. The results suggest different criteria that could help policymakers in designing policies with a higher propensity to achieve the desired goal. Using this tool, the policy decision-makers can better test/predict whether the target will be achieved and to what degree. The novelty in the present paper is the application of Machine Learning methods (through the Decision Trees) for energy policy analysis. Machine learning methodologies present an alternative way to pilot RE policies before spending lots of time, money, and other resources. We also find that using Machine Learning techniques underscores the importance of data availability. A general summary for policymakers has been included.

Abstract
Despite being one of the most cost-effective methods, email marketing remains challenging due to the low rate of opened emails and the high percentage of unsubscribed campaigns. Since the sender and the subject line are the only information that the recipient sees at first when receiving an email, the decision to open an email critically depends on these two factors, which should stand out and catch the recipient's attention. Therefore, the motivation behind this study is to support email campaign editors in choosing a subject line based on its potential quality. We propose and compare several models to measure the quality of a subject line, considering its potential to promote the email opening. The subject lines' structure and content are explored together with different machine learning techniques (Random Forest, Decision Trees, Neural Networks, Naive Bayes, Support Vector Machines, and Gradient Boosting). To validate the proposed model, a data set of 140,000 emails' subject lines was used. The results revealed that the models proposed are very promising to support the definition of the email marketing subject lines and show that the combination of data regarding the structure, the content of the subject lines, and senders characteristics leads to more accurate classifications of the potential of the subject line.