Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System

One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes heating and cooling powers to individual temperature zones. The controllers of both layers employ regression-based error estimation to predict and incorporate the model error. The proposed approach is evaluated in a software-in-the-loop simulation using a physics-based digital twin model. Simulation results show the efficacy and robustness of the proposed approachComment: 8 pages, 4 figures. To be published in 2023 IEEE Conference on Control Technology and Applications (CCTA) proceeding

Implicit Incorporation of Heuristics in MPC-Based Control of a Hydrogen Plant

The replacement of fossil fuels in combination with an increasing share of renewable energy sources leads to an increased focus on decentralized microgrids. One option is the local production of green hydrogen in combination with fuel cell vehicles (FCVs). In this paper, we develop a control strategy based on Model Predictive Control (MPC) for an energy management system (EMS) of a hydrogen plant, which is currently under installation in Offenbach, Germany. The plant includes an electrolyzer, a compressor, a low pressure storage tank, and six medium pressure storage tanks with complex heuristic physical coupling during the filling and extraction of hydrogen. Since these heuristics are too complex to be incorporated into the optimal control problem (OCP) explicitly, we propose a novel approach to do so implicitly. First, the MPC is executed without considering them. Then, the so-called allocator uses a heuristic model (of arbitrary complexity) to verify whether the MPC's plan is valid. If not, it introduces additional constraints to the MPC's OCP to implicitly respect the tanks' pressure levels. The MPC is executed again and the new plan is applied to the plant. Simulation results with real-world measurement data of the facility's energy management and realistic fueling scenarios show its advantages over rule-based control.Comment: 8 pages, 3 figures. To be published in IEEE 3rd International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE 2023) proceeding

Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

We present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems, i.e., selecting a solution from the Pareto front. In the first step, a knee region is determined based on the normalized Euclidean distance from a hyperplane defined by the furthest Pareto solution and the negative unit vector. The size of the knee region depends on the Pareto front’s shape and a design parameter. In the second step, preferences for all objectives formulated by the decision maker, e.g., 50–20–30 for a 3D problem, are translated into a hyperplane which is then used to choose a final solution from the knee region. This way, the decision maker’s preference can be incorporated, while its influence depends on the Pareto front’s shape and a design parameter, at the same time favorizing knee points if they exist. The proposed approach is applied in simulation for the multi-objective model predictive control (MPC) of the two-dimensional rocket car example and the energy management system of a building

Application of Pareto Optimization in an Economic Model Predictive Controlled Microgrid

This paper presents an economic model predictive control approach for a linear microgrid model. The microgrid in grid-connected mode represents a medium-sized company building including storage systems, renewable energies and couplings between the electrical and heat energy system. Economic model predictive control together with Pareto optimization is applied to find suitable compromises between two competing objectives, i. e. monetary costs and thermal comfort. Using real-world data from 2018 and 2019, the model is simulated with auto-detection of the Pareto solution which is closest to the Utopia point. The results show that the Pareto optimization can either be used in real-time control of the microgrid, or to obtain suitable weights from long term simulations. Both approaches result in significant cost reductions

Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning

Average human behavior in cue combination tasks is well predicted by Bayesian inference models. As this capability is acquired over developmental timescales, the question arises, how it is learned. Here we investigated whether reward dependent learning, that is well established at the computational, behavioral, and neuronal levels, could contribute to this development. It is shown that a model free reinforcement learning algorithm can indeed learn to do cue integration, i.e. weight uncertain cues according to their respective reliabilities and even do so if reliabilities are changing. We also consider the case of causal inference where multimodal signals can originate from one or multiple separate objects and should not always be integrated. In this case, the learner is shown to develop a behavior that is closest to Bayesian model averaging. We conclude that reward mediated learning could be a driving force for the development of cue integration and causal inference

Computational Audiovisual Scene Analysis in Online Adaptation of Audio-Motor Maps

Yan R, Rodemann T, Wrede B. Computational Audiovisual Scene Analysis in Online Adaptation of Audio-Motor Maps. IEEE Transactions on Autonomous Mental Development. 2013;5(4):273-287.For sound localization, the binaural auditory system of a robot needs audio-motor maps, which represent the relationship between certain audio features and the position of the sound source. This mapping is normally learned during an offline calibration in controlled environments, but we show that using computational audiovisual scene analysis (CAVSA), it can be adapted online in free interaction with a number of a priori unknown speakers. CAVSA enables a robot to understand dynamic dialog scenarios, such as the number and position of speakers, as well as who is the current speaker. Our system does not require specific robot motions and thus can work during other tasks. The performance of online-adapted maps is continuously monitored by computing the difference between online-adapted and offline-calibrated maps and also comparing sound localization results with ground truth data (if available). We show that our approach is more robust in multiperson scenarios than the state of the art in terms of learning progress. We also show that our system is able to bootstrap with a randomized audio-motor map and adapt to hardware modifications that induce a change in audio-motor maps