Automated Planning and Scheduling is a central research area in AI that has been studied since the inception of the field and where European research has been making strong contributions over decades. Planning is a decision-making technology that consists in reasoning on a predictive model of a system being controlled and deciding how and when to act in order to achieve a desired objective. It is a relevant technology for many application areas that need quick, automated and optimal decisions, like agile manufacturing, agrifood or logistics. Although there is a wealth of techniques that are mature in terms of science and systems, several obstacles hinder their adoption, thus preventing them from making the footprint on European industry that they should make. For example, it is hard for practitioners to find the right techniques for a given planning problem, there are no shared standards to use them, and there is no easy access to expertise on how to encode domain knowledge into a planner.
The AIPlan4EU project will bring AI planning as a first-class citizen in the European AI On-Demand (AI4EU) Platform by developing a uniform, user-centered framework to access the existing planning technology and by devising concrete guidelines for innovators and practitioners on how to use this technology. To do so, we will consider use-cases from diverse application areas that will drive the design and the development of the framework, and include several available planning systems as engines that can be selected to solve practical problems. We will develop a general and planner-agnostic API that will both be served by the AI4EU platform and be available as a resource to be integrated into the users’ systems. The framework will be validated on use-cases both from within the consortium and recruited by means of cascade funding; moreover, standard interfaces between the framework and common industrial technologies will be developed and made available.
AIPlan4EU is centered around 6 ambitious objectives:
O1: Making planning accessible to practitioners and innovators
O2: Facilitate the integration of planning and other ICT technologies
O3: Making planning relevant in diverse application sectors
O4: Seamlessly integrate planning within the AI4EU platform
O5: Facilitate learning of planning for reskilling and lower the access barrier
O6: Standardize and drive academic research towards applications
Accurate estimations of energy data represents a preliminary task in data analytics methodologies aimed at enhancing the energy performance of all systems in the value chain. In fact, predictive tools are capable to effectively extract energy patterns from building-related data, enabling valuable opportunities such as:
● The detection of unexpected trends of energy consumption during operation.
● The optimization of management strategies for reducing the mismatch between energy demand and energy generation.
● The implementation of demand response and alternative management strategies.
● The definition of robust energy performance baselines for the assessment of energy saving achieved through the implementation of retrofitting actions in existing buildings.
● The detection of power failures in case of earthquake, fire, flood disasters and high energy consumption rates.
In this context, our team is developing an innovative energy storage system based on li-ion batteries for residential units, public and private buildings and offices. The main purpose of the storage application is to ensure continuity in energy supply for strategic and vulnerable facilities, such as hospitals, in emergency situations. Our system protects the customer against unpredictable electricity cuts, especially in case of disasters (earthquakes, floods and fire risks) by providing management and recovery solutions.
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