Peter Hellinckx
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Activation strategies for decentralized booster heat pumps based on dynamic pricing models Open
Achieving carbon-neutral energy supplies in collective heating systems is increasingly feasible through the use of renewable energy sources and optimized heating capacities. Within apartment buildings, a combined heat distribution circuit …
Self-Attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts Open
Current postprocessing techniques often require separate models for each lead time and disregard possible interensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we t…
Predictability of Wind Ramping Events in the Belgian Offshore Zone: Insights from NWP Models and Post-processing Open
Offshore wind capacity in the Belgian Offshore Zone (BOZ) is currently 2262 MW. The increasing reliance on wind energy highlights the need for accurate forecasting and effective energy dispatch. Rapid changes in wind power, known as wind p…
Enhancing Renewable Energy Forecasting: A Comprehensive Evaluation of Weather Forecast Models and Post-Processing Methods for Belgium Open
As renewable energy sources continue to account for an increasing proportion of Belgium's energy production, decision making in renewable energy production increasingly relies on accurate numerical weather prediction forecasts. For general…
Attention-based postprocessing of ensemble weather forecasts for renewable energy applications by leveraging inter-ensemble relationships of multiple predictors Open
Indirect models for renewable energy forecasting rely heavily on accurate weather predictions. Operational weather forecasting today is mainly based on numerical weather prediction models, often employing ensembles to estimate the day-to-d…
The Impact of Commonly Overlooked Faults in Combined Heat Distribution Circuits: Analysis of Heat Meter Data Open
This study analyses the in-situ performance of Combined Heat Distribution Circuit systems and Heat Interface Units to address commonly overlooked faults. Using data from energy meters, including gas meters and automated heat meter readings…
Automatic Identification and Evaluation of Building Clusters for Optimal District Heating Networks Phasing Open
Considering future city changes when designing district heating networks can be tedious. This paper tackles this problem by automatically identifying potential future building clusters connections, and evaluating relevant financial and pla…
Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications Open
A myriad of techniques regarding renewable energy forecasting have been proposed in recent literature, commonly classified as physical, statistical, machine learning based or a hybrid form thereof. The renewable energy forecasting process …
Enabling space cooling in combined heat distribution circuits by grouping same temperature demands Open
The European Commission reports that the relative shares of DHW and space cooling in the total energy demand of the building stock is rising in Europe and that the cooling market increases by 1 to 2% a year. Therefore, research on the cool…
Reduced energy cost of heat-pump driven heating systems by smart use of thermal storage. Open
The main challenge of the energy sector is to provide sustainable energy at an affordable cost without compromising the security of supply. In this respect, heat pumps are being deployed in the built environment. However, their sustainabil…
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning Open
Many multi-agent systems require inter-agent communication to properly achieve their goal. By learning the communication protocol alongside the action protocol using multi-agent reinforcement learning techniques, the agents gain the flexib…
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning Open
Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment. The most common approach to allow learned communication between agents is the use of a differentiable com…
Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning Open
Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Netherlands as an essent…
Grouped Charging of Decentralised Storage to Efficiently Control Collective Heating Systems: Limitations and Opportunities Open
Collective heating systems have multiple end-users with time-varying, often different temperature demands. There are several concepts catering to this, e.g., multi-pipe networks and 2-pipe networks with or without decentralised booster sys…
An Energy Management Unit for Predictive Solar Energy Harvesting IoT Open
As the need for stand-alone energy harvesting devices increases, the alleviation of the ecological and economic impact of their production and maintenance is possible by increasing battery life while reducing needed battery capacity.Howeve…
Metamodelling of Noise to Image Classification Performance Open
Machine Learning (ML) has made its way into a wide variety of advanced applications, where high accuracies can be achieved when these ML models are evaluated in the same context as they were trained and validated on. However, when these hi…
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways Open
In recent years, interest in autonomous shipping in urban waterways has\nincreased significantly due to the trend of keeping cars and trucks out of city\ncenters. Classical approaches such as Frenet frame based planning and potential\nfiel…
Resource efficient AI: Exploring neural network pruning for task specialization Open
This paper explores the use of neural network pruning for transfer learning applications for more resource-efficient inference. The goal is to focus and optimize a neural network on a smaller specialized target task. With the advent of IoT…
Application Placement in Fog Environments using Multi-Objective Reinforcement Learning with Maximum Reward Formulation Open
The service placement problem considers the placement of multiple connected services across a heterogeneous device network and is one of the core problems of fog computing. We discuss the complexity of this service placement problem, and p…
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning Open
Communication is crucial in multi-agent reinforcement learning when agents are not able to observe the full state of the environment. The most common approach to allow learned communication between agents is the use of a differentiable com…
Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model Open
Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with s…
Requirements and Specifications for the Orchestration of Network Intelligence in 6G Open
Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. Network Intelligence (NI) will be the key enabler for such a vision, empowering myriad of orchestrators and controllers across network domai…