Reinforcement Learning for Joint Design and Control of Battery-PV Systems
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Energy planning recently received more attention in Switzerland through the new strategy phasing out nuclear energy by 2034. Often however the energy planning is only done from the electrical side. This work takes a different angel and helps communities an ...
We disprove a conjecture in Density Functional Theory, relative to multimarginal optimal transport maps with Coulomb cost. In the case of spherically symmetric data, which model for instance Lithium and Beryllium atoms, we show that some special maps, intr ...
Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime impor ...
This article is an attempt to represent Big Data research in digital humanities as a structured research field. A division in three concentric areas of study is presented. Challenges in the first circle – focusing on the processing and interpretations of l ...
In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time-consuming undertaking. When a set of input/output measurements is available, one can derive the controller directly f ...
An optimization method based on Mixed Integer Linear Programming (MILP) has been developed for simultaneous optimization of water and energy (SOWE) in industrial processes. The superstructure integrates process thermal streams and optimizes the consumption ...
Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input-output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, ...
The promise of Bayesian methods for big data sets has not fully been realized due to the lack of scalable computational algorithms. For massive data, it is necessary to store and process subsets on different machines in a distributed manner. We propose a s ...
Long-term planning for energy systems is often based on deterministic economic optimization and unreliable forecasts of fuel prices. Usual consequence is a low penetration of renewables and more efficient technologies in favor of fossil alternatives. A cla ...
Crowdsourcing is widely proposed as a method to solve large variety of judgement tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tende ...