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143 lines
8.1 KiB
143 lines
8.1 KiB
3 years ago
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title: "Long Term Research Topics"
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editor: visual
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---
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The overall scientific aim of the lab is to create, test and implement accurate and reliable AI solutions to improve the efficiency and effectivity of water and energy production and distribution. This will be revolutionary for the Dutch Caribbean Islands. To achieve the scientific goal, we have formulated five scientific topics and challenges:
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1. Achieving a proper balance between human operators and AI algorithms (hybrid AI),
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2. Developing forecasting algorithms that are both accurate and reliable,
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3. Realizing predictive maintenance involving time-to-failure predictions,
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4. Developing AI for power grid balancing using recommendation enhanced Demand Response, and
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5. Understanding and advancing the readiness to accept the use of AI both within the critical infrastructure and the general population.
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### Project 1
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> #### Artificial Intelligence for decision support in water desalination, recycling, and purification
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##### Scientific challenge:
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::: grid
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::: g-col-8
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For over 30 years, AI and computational intelligence have been used in water desalination domain (He et al., 2022) for applications like support in the decision making, prediction, optimization, and control with respect to alarm processing, fault detection, load forecasting, and security assessment.
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<details>
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<summary>Click to Read More!</summary>
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The recent trend to use renewable energy sources for desalination and wastewater treatment makes the decision process more complicated, due to the temporal variability of these sources (Cabrera and Carta, 2019; Harrou et al., 2018; Cheng et al, 2020). AI systems that go beyond the current point solutions are needed to deal with this complexity, while considering a system-wide view and a balanced interaction between the AI systems and human experts. The scientific challenge of this PhD project is to develop hybrid AI solutions that combine advanced prediction methods (e.g. deep learning algorithms), multi-objective optimization and adaptive models with explainable AI decision-making methods from computational intelligence to realize such a balance.
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</details>
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::: g-col-4
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:::
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> **PhD promoters**: Dr. Laura Genga and Prof.Dr.Ir. Uzay Kaymak
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### Project 2
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> #### Artificial Intelligence for power load and renewable energy forecasting in electricity grids
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##### Scientific challenge
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::: grid
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::: g-col-8
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Short-term forecasts of (a) power load and (b) renewable energy supply, are crucial for decarbonising electricity grids: without these forecasts, high-carbon baseload generators must be kept running.
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<details>
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<summary>Click to Read More!</summary>
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The scientific challenge is to achieve accurate and reliable forecasts, in the face of changeable energy demand patterns and external covariates (weather, public events, etc). Deep learning has been shown to perform very well on power-load forecasting and achieves promising results on renewable-energy forecasting (Wang et al., 2019). This PhD plan sets out to develop deep learning algorithms that realize forecasts that are both accurate and reliable, with a flexibility to adapt to local conditions.
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</details>
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::: g-col-4
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> **PhD promoters**: Dr. Dan Stowell and Dr. Ciçek Güven
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### Project 3
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> #### Artificial Intelligence for predictive maintenance in water and electricity infrastructure
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##### Scientific challenge
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::: grid
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::: g-col-8
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Predictive maintenance offers great potential value to the energy and water supply industry (cf. SDG7). Timely detection of required maintenance of machines, sensors, or other critical infrastructure can prevent disruptions of service and costly loss of resources.
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<details>
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<summary>Click to Read More!</summary>
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For instance, visual sensors can be used to analyze and detect subtle patterns (Hendrix et al. 2021; van Lieshout, van Oeveren, van Emmerik, & Postma, 2020; Noord and Postma, 2017) and auditory sensors can pick up subtle changes in sounds (Buisman & Postma, 2012). More generally, artificial intelligence offers improved prediction performance on predictive maintenance tasks. Recent advances in visual object recognition and auditory analysis allow for a continuous and reliable monitoring of system states. In particular, the focus will be on self-supervised and unsupervised learning (see e.g. Olier et al., 2018). In the absence of supervisory labels, adequate priors will be acquired using large unlabeled datasets (see e.g. Ding et al., 2022). In the context of Industry 4.0, predictive maintenance leads to numerous innovations. One of the main challenges is to deal with real time-based predictive maintenance (Zonta et al., 2020). Instead of treating predictive maintenance as a simple alert monitoring, real time-based predictive maintenance offers an estimate of time-to-failure.
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</details>
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::: g-col-4
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> **PhD promoters**: Dr. Sebastian Olier and Prof. Dr. E.O. Postma
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### Project 4
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> #### AI for power grid balancing using recommendation-enhanced Demand Response
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##### Scientific challenge
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::: grid
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::: g-col-8
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To improve grid balancing (SDG7), esp. in case of many renewable energy resources and fluctuating demand, AI forecasting methods (WP2) can be combined with Demand Response (DR) methods. DR motivates energy consumers in some way (e.g. pricing-based) to adjust their energy usage to the available energy resources and demand.
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<details>
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<summary>Click to Read More!</summary>
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Smart grid technology allows DR to be more data-driven and a multitude of AI technologies have already been applied to DR (Antonopoulos et al., 2020), including ML, deep learning and agent-based approaches. However, little research has investigated the consumer side of DR apart from simple customer segmentation approaches (Antonopoulos et al., 2020). Rather than having consumers (household or industry) passively follow the DR (pricing) scheme, AI technology such as recommender algorithms could play an active role in recommending consumers how and when to distribute their energy usage and return based energy forecasts and DR information. Such tailored interventions to improve DR approaches and optimize dynamic grid balancing.
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</details>
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::: g-col-4
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> **PhD promoters**: Dr.Ir. Martijn Willemsen and Dr. Claudia Zucca
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### Project 5
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> #### Social support for the real-world introduction of AI in critical infrastructure
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##### Scientific challenge
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::: grid
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::: g-col-8
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The social impact of technology on its users has been vastly proved to be enormous (King and He, 2006) since it might pose organizational and social obstacles. Acceptance of new technologies, especially in the energy field, has been recognized as one of the primary barriers to implementing technological innovations (Huijts, Molin, and Steg, 2012).
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<details>
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<summary>Click to Read More!</summary>
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In the context of the ILUSTRE project, the impact of IT technology on the energy and water supply domains is twofold. First: the impact on the partners industry managers and employees. The implementation creates disruption, and unless managers support the innovation and workers understand and comply with the new infrastructure, they might actively oppose the implementation. Second: the impact on the broader society since AI technology will affect the quality and the features of the services provided to the population. The scientific challenge addressed in this Ph.D.-project is to employ group model building (GMB) and social network analysis (SNA) to monitor the extent to which the employees and the larger public (together called the stakeholders) receive and respond to the implementation. GMB is a widely used approach to collect data and monitor (and influence) the opinions and sentiments of groups of stakeholders (Peck, 1998). SNA has been used extensively to analyze the group dynamics at the roots of technological implementation reception (Sasovova and Leenders, 2009). These techniques can well be used in conjunction with agent-based simulation models.
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</details>
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:::
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::: g-col-4
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:::
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> **PhD promoters**: Dr. Claudia Zucca and prof. Dr. Roger Leenders
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