Abstract

Abstract
Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a k-fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests-McNemar's and Wilcoxon signed rank-in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.

Abstract

Abstract
Smart4RES is a European Horizon2020 project developing next generation solutions for renewable energy forecasting. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the proposed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management. © 2021 Energynautics GMBH.

Abstract
In recent years, traditional power systems have undergone a significant transition, mainly related to the massive penetration of renewable generation. More specifically, the transformation of residential consumers into prosumers has been challenging the existing operation of the electricity market. This transition brings new challenges and opportunities to the power system, leading to new business models. One widely discussed change is related to a consumer-centric or prosumer-driven approach, promoting increased participation of small consumers in power systems. The present paper aims at discussing the recent business models as enablers of the increasing prosumers' role. To do so, it defines the main features of prosumers and their related regulation as well as possible market designs within power systems. In addition, it discusses enabling technologies to properly create the conditions that sustain new prosumer-driven markets. Then, it presents a comprehensive review of existing and innovative business models and a discussion on their future roles in modern power systems. Moreover, a set of recommendations for promoting these business models in the power system is provided. An important conclusion is that, even though economically possible, not all innovative business models can spread around the world due to regulatory obstacles.

Abstract
The global trend guided by the energy systems decarbonization, decentralization and digitalization combined with the increase of distributed Renewable Energy Sources (RES) are allowing prosumers to take a more active role in the electricity markets. In this context, a market structure based on Peer-to-Peer (P2P) transactions is very promising but presents challenges for the network's operation. A critical challenge is to ensure that network constraints are not violated during energy trade between peers. Thus, the main contribution of this paper is the development of a methodology for the optimization of P2P energy transactions, accounting for network operation. The paper proposes a three-step approach (P2PTDF), using Topological Distribution Factors (TDF) to penalize peers responsible for violations that may occur, ensuring a feasible solution. Simulations were performed with the modified IEEE 14-bus system with 19 peers, including the possibility of exchanging energy with an external grid.