Abstract
Energy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy. © 2023 IEEE.

Abstract

Abstract
Robotics and intelligent systems are key technologies to promote efficient and innovative applications in the most diverse domains (industry, healthcare, agriculture, construction, mobility, etc [...]

Abstract
The objective of this work is to evaluate the effects of different energy policies designed to favor decarbonization by increasing renewable sources. In particular, the implementation of energy efficiency policies and the application of hourly differential electricity tariffs. The Open-Source Energy Modelling System (OSeMOSYS) has been adopted to visualize the effects of each of the actions in the short, medium, and long term, from 2024 till 2046. From our results, the application of hourly differentiation tariffs does not favor either the increase in the implementation of renewable sources or decarbonization processes. The implementation of energy efficiency policies (1-1.25% annual demand decrease), in the long term, allows to reach 80% of energy production from renewable sources. In all the scenarios, the energy sources with a greater level of intermittency, such as wind or solar, strongly increased their contribution in the medium-term, thereby stabilizing their long-term contribution. Finally, the implementation of photovoltaic solar energy becomes necessary only in the long-term. It seems clear that this contribution, up to 20% of the renewable, is associated with the nuclear blackout.

Abstract
Language-guided generative models are increasingly used in audiovisual production. Image diffusion allows for the development of video sequences and some of its coordination can be established by text prompts. This research automates a video production pipeline leveraging CLIP-guidance with longform text inputs and a separate text-to-speech system. We introduce a method for producing frame-accurate video and audio summaries using a virtual timeline and document a set of video outputs with diverging parameters. Our approach was applied in the production of the film Irreplaceable Biography and contributes to a future where multimodal generative architectures are set as underlying mechanisms to establish visual sequences in time. We contribute to a practice where language modelling is part of a shared and learned representation which can support professional video production, specifically used as a vehicle throughout the composition process as potential videography in physical space.

Abstract
Distributed Machine Learning, in which data and learning tasks are scattered across a cluster of computers, is one of the answers of the field to the challenges posed by Big Data. Still, in an era in which data abounds, decisions must still be made regarding which specific data to use on the training of the model, either because the amount of available data is simply too large, or because the training time or complexity of the model must be kept low. Typical approaches include, for example, selection based on data freshness. However, old data are not necessarily outdated and might still contain relevant patterns. Likewise, relying only on recent data may significantly decrease data diversity and representativity, and decrease model quality. The goal of this paper is to compare different heuristics for selecting data in a distributed Machine Learning scenario. Specifically, we ascertain whether selecting data based on their characteristics (meta-features), and optimizing for maximum diversity, improves model quality while, eventually, allowing to reduce model complexity. This will allow to develop more informed data selection strategies in distributed settings, in which the criteria are not only the location of the data or the state of each node in the cluster, but also include intrinsic and relevant characteristics of the data.