In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances-such as the COVID-19 pandemic-can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures-namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese national 15-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models; (ii) to become aware of the serious consequences of crisis events on model performance; (iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context.

In the 21st century, technology evolves extremely fast. The same applies to technology-related professions, mostly in terms of skills requirements. Contradictorily, higher education technological institutions are not always in the position to keep up with the labor market requirements. As a result, some of the skills taught in their courses are oftentimes outdated. From a learner’s perspective, the main goal should be to avoid such outdated courses, as for most university students, the long-term objective is to land a job, where they will utilize the skills they acquired from their studies. On the other hand, from an educational decision maker’s perspective, the most important goal is to keep up with the changes in the labor market, offering courses that will be valuable for the prospective careers of students. The work conducted in the context of this publication aims to bridge the gap between education offered in universities and job market skills’ requirements in technology. Specifically, a skill and course recommender system was developed to help learners select courses that are valuable for the job market, as well as a curriculum design service, which recommends updates to a given curriculum based on the job market needs. Both services are built on top of a text mining service that retrieves job posts from several online sources and performs skill extraction from them based on text analytics techniques. Moreover, a decision support service was developed to facilitate optimal decisions for both learners and education decision makers. All services were evaluated positively by 31 early users.

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Nowadays, recommender systems (RS) are no longer evaluated only for the accuracy of their recommendations. Instead, there is a requirement for other metrics (e.g., coverage, diversity, serendipity) to be taken into account as well. In this context, the multi-stakeholder RS paradigm (MSRS) has gained significant popularity, as it takes into consideration all beneficiaries involved, from item providers to simple users. In this paper, the goal is to provide fair recommendations across item providers in terms of diversity and coverage for users to whom each provider’s items are recommended. This is achieved by following the methodology provided by the literature for solving the recommendation problem as an optimization problem under constraints for coverage and diversity. As the constraints for diversity are quadratic and cannot be solved in sufficient time (NP-Hard problem), we propose a heuristic approach that provides solutions very close to the optimal one, as the proposed approach in the literature for solving diversity constraints was too generic. As a next step, we evaluate the results and identify several weaknesses in the problem formulation as provided in the literature. To this end, we introduce new formulations for diversity and provide a new heuristic approach for the solution of the new optimization problem.

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