PEOPLEAnton Soppelsa

de en it

Anton Soppelsa

Senior Researcher
Institute for Renewable Energy

T +39 0471 055 623

anton.soppelsa@eurac.edu

https://orcid.org/0000-0002-8073-9045

download or scan my contact information (vCard Format)

About

I am an Automation Engineer with a Master Degree in Computer Engineering and a Ph.D. in Electrical Engineering.

My research deals with the industrial development of the control systems used within heating and cooling facilities, both at hardware and software level.
In paticular, it focuses on the application of Machine Learning and System Identification techniques to improve their design and operation.

At Eurac Research I contributed to the design and implementation of the control and data acquisition system of the Energy Exchange experiment and the Heat Pump Lab climatic chamber and I lead the design, development and LabView implementation the software operating them.

In the course of my career I had the privilege to share with students of the University of Padova and Bolzano part of my expertise in the C/C++ programming language, embedded systems programming, automatic controls and PLC programming, as teaching assistant or lead instructor.

Scientific Publications

Journal article2026

A Two-Degree-of-Freedom Controller with Transport Delay Compensation for Application in Thermo-Hydraulic Circuits

Soppelsa A, Fedrizzi R, Pipiciello M

Anton Soppelsa, Mauro Pipiciello

This paper presents the development and implementation of a two-degree-of-freedom, model-based controller designed to enhance the efficiency and flexibility of a certain class of circuits used in thermo-hydraulic applications. The controller addresses significant challenges such as time-variable transport delays and actuator coupling, which are common in dynamic testing environments. By utilising a model-based control approach and the Smith predictor configuration, the proposed controller simultaneously tracks supply temperature and mass flow rate with improved performance compared to the proportional–integrative–derivative (PID) controller used in our laboratory. The system’s effectiveness is demonstrated through a virtual hydraulic complex model developed in the OpenModelica environment and experimental tests, following the implementation of the controller in the laboratory real-time control software. Both the simulation and experimental results indicate that the controller can closely follow pre-programmed temperature and flow rate waveforms while effectively rejecting disturbances, with an RMSE reduction of up to about 80% under the specific test protocol used in this work, making it suitable for applications required to deal with the above constrains, such as laboratory dynamic testing and thermo-mechanical and chemical process regulation.

DOI Fonte

DOI: 10.3390/en19092128

Journal article2025

An adaptive multi-seasonal ARIMA approach for domestic hot water load forecasting: A pilot study

Nagpal H, Soppelsa A, Madsen H, Fedrizzi R

Himanshu Nagpal, Anton Soppelsa

Progetto: EU H2020 HAPPENING: Pompe di calore per condomini esistenti per raggiungere gli obiettivi Comunitari in termini energetici e ambientali

The European Commission has consistently emphasised the importance of decarbonising the building sector by introducing various building and energy-related directives. The research community shares this sentiment and has continuously put effort into optimising energy management in buildings. One key requirement for intelligent energy management approaches, such as model predictive control (MPC), is the availability of accurate load predictions. This allows the controller to anticipate the system’s behaviour, facilitating proactive decision-making to reduce energy consumption and emissions. In this context, this paper presents a multi-seasonal autoregressive integrated moving average (mSARIMA) approach for forecasting the domestic hot water (DHW) load. mSARIMA is a novel extension of the traditional SARIMA approach and can handle multiple seasonalities in a time-series dataset. Furthermore, the work presents a well-structured dynamic system formulation of the mSARIMA approach using a state-space representation, allowing efficient forecasting and easier integration into real-time decision-making systems. With its adaptive nature, the mSARIMA model is retrained at regular intervals using newly available measurements and parameters are re-estimated. For parameter estimation, the maximum likelihood method was employed, minimising the negative log-likelihood of the joint distribution of residuals. The model delivers reliable predictions, with an average root mean squared error and average percentage absolute bias of 2.1 kW and 8.2 %, respectively, over the simulation period with a 24-h prediction horizon. Finally, the model is implemented as software in the control hardware of a pilot site and undergoes real-time testing and validation. The model’s training process is quick, while the prediction process is instantaneous, with sufficiently accurate predictions.

DOI Fonte

DOI: 10.1016/j.enbuild.2025.116226

Journal article2025

On-Site temperature and irradiance forecast tuning for improved load prediction in buildings

Jarraya A, Diller T, Nagpal H, Soppelsa A, Trentin F, Henze G, Fedrizzi R

Himanshu Nagpal, Anton Soppelsa, Federico Trentin, Tim Christoph Diller, Amine Jarraya

Progetto: EU H2020 HAPPENING: Pompe di calore per condomini esistenti per raggiungere gli obiettivi Comunitari in termini energetici e ambientali

Building management systems (BMSs) with predictive control strategies rely on accurate weather forecasts to optimise heating and cooling operations. These strategies depend on precise climatic inputs to adjust system operations dynamically. Typically, weather forecast data is sourced from the internet and is generated by numerical weather prediction (NWP) models using advanced mathematical simulations. However, these models fail to account for localised nano-climatic variations, such as significant temperature and irradiance differences between the north and south sides of a building or the actual environmental conditions around the on-site sensors. These nano-climatic effects directly influence the calculation of the future thermal load of the building, which is crucial for predictive control approaches. To address this challenge, we propose a hybrid methodology that integrates NWP forecasts with local measurements from on-site sensors, improving NWP forecast accuracy. Our approach employs Inverse Distance Weighting (IDW) to interpolate NWP outputs to a specific geographical position and applies exponential smoothing for further finetuning by using historical error patterns. This methodology enhances the predictive accuracy of temperature and irradiance forecasts, achieving reductions of up to 60% to 80% in temperature errors and up to 20% to 30% in irradiance errors. Based on the finetuned weather forecast, the accuracy of building’s thermal load prediction is improved up to 86% compared to the predictions with IDW weather forecast.

DOI Fonte

DOI: 10.1016/j.enbuild.2025.115642

Journal article2024

A dynamic programming based method for optimal control of a cascaded heat pump system with thermal energy storage

Diller T, Soppelsa A, Nagpal H, Fedrizzi R, Henze G

Himanshu Nagpal, Anton Soppelsa, Tim Christoph Diller

The residential heating and cooling sector has been increasingly electrifying, predominantly using electrically driven heat pumps (HP) in combination with thermal/electrical energy storage systems. While these developments contribute to increased renewable and low carbon energy shares in the sector, exploiting the full potential of the technology requires a smart control of these systems that can account for predicted renewable energy availability in the future and the corresponding HP system performance. However, modelling a system featuring complex internal dynamics, in a way that is suitable for smart control, is challenging. Models need to be sophisticated enough to accurately capture the system's nonlinearities and intricacies while at the same time fast enough to enable a thorough search of the solutions space, in suitable computational time. Dynamic programming (DP) is a promising approach to smart controls, as it combines the ability to use complex, non-linear models while being an exhaustive search algorithm, guaranteeing that the global optimum is found. This paper presents an innovative modelling framework that entails reduced order models (ROM) of an HP substation's main components (i.e., HP and thermal energy storage—TES), elaborated in a fashion suitable for use in DP; these have been shaped as to include significant physical operating constraints (e.g., HP compressor variable speed, non-linear coefficient of performance—COP—dependency on outdoor and distribution temperature) affecting the system performance, while at the same time minimising the amount of state variables (i.e., TES temperatures, HP thermal and electric capacity) the optimizer needs to handle. In an application to an exemplary HP system, our system models compare remarkably well to detailed TRNSYS counterparts, used as a reference ground truth. The system achieves significant cost-saving enabled by the dynamic programming optimization approach, facilitating a 13% decrease in power consumption compared to conventional rule-based control.

DOI Fonte

DOI: 10.1007/s11081-023-09853-5

Journal article2024

Numerical and experimental investigation of stratified water storage tanks: An enhanced adaptive-grid model

Jodeiri AM, Soppelsa A, Turrin F, Pipiciello M, Cozzini M, Fedrizzi R

Anton Soppelsa, Mauro Pipiciello, Francesco Turrin, Marco Cozzini

Progetto: EU H2020 REWARDHeat - Recupero di calore rinnovabile e di scarto in reti di teleriscaldamento e raffreddamento

Stratified water storage tanks are key in thermal energy systems, effectively balancing energy supply with heat demand, thus facilitating operational flexibility. Accurately modeling both energy balance and thermocline evolution with minimal computational effort is essential for models implementation in real-time control applications. One-dimensional, static-grid models are favored for their easy implementation; however, they require dense grids in order to minimize numerical diffusion and to represent mixing effect correctly, resulting in rather expensive computational effort. This study introduces an adaptive-grid model that comprehensively represents the water storage tank’s geometrical features, including inlet and outlet duct heights and dead volume sizes. It also details the spatially discretized heat diffusion and characterizes the inflow region’s mixing using an empirical equation. Implemented in both TRaNsient SYstem Simulation (TRNSYS) and Modelica environments, the model is validated through laboratory experiments with a hot water storage tank operating under various mass flow and temperature conditions, representative of heat pump-driven heating systems. The results indicate improvements in computational efficiency compared to static-grid models, specifically a 55% reduction in simulation duration with the same level of uncertainty.

DOI Fonte

DOI: 10.1016/j.applthermaleng.2024.123113

Journal article2024

The bidirectional substation for district heating users: experimental performance assessment with operational profiles of prosumer loads and distributed generation

Pipiciello M, Trentin F, Soppelsa A, Menegon D, Fedrizzi R, Ricci M, Di Pietra B, Sdringola P

Diego Menegon, Anton Soppelsa, Mauro Pipiciello, Federico Trentin

The regulatory European and national policies outline the role of efficient district heating in achieving mid and long-term decarbonization targets. Prosumers could be equipped with bidirectional exchange substations to increase the local production contribution. The present paper evaluates the contribution of a bidirectional substation in maximizing the use of thermal energy produced locally by renewable sources or waste heat in a densely populated area, considering the limitation due to the non-contemporaneity of production and consumption and possibilities to overcome them. The substation prototype and the control system were tested in operational conditions, assuming the district heating network operating at two temperature levels (80 °C/50 °C and 60 °C/30 °C for supply and return) and applying the hardware-in-the-loop technique for coupling the substation in real-time with data-driven thermal loads (multi-family residential building) and production (solar panels and waste heat) profiles. The various energy flows in the substation are presented for representative days, resulting in a district heating contribution to the user’s load ranging between 99.8 % (winter day) and 21.8 % (summer day). The net-metering mechanism was considered, and the assessment extended to the entire year. The local production contribution to the user’s load ranges between 24 % and 100 %, depending on the system configuration considered.

DOI Fonte

DOI: 10.1016/j.enbuild.2023.113872

Journal article2023

Adaptive thermal load prediction in residential buildings using artificial neural networks

Fouladfar MH, Soppelsa A, Nagpal H, Fedrizzi R, Franchini G

Himanshu Nagpal, Anton Soppelsa

Progetto: EU H2020 REWARDHeat - Recupero di calore rinnovabile e di scarto in reti di teleriscaldamento e raffreddamento

Accurate prediction of thermal load in buildings is essential for efficient energy planning. In this study, we investigate the application of Artificial Neural Networks (ANNs) to predict thermal load and indoor temperature evolution in residential buildings. We propose a flexible and adaptive model that is retrained on a daily basis to deliver updated hour-by-hour predictions for the following 24 h. To strike a balance between prediction accuracy and computational efficiency, we employ various design choices, including feature selection using Pearson's correlation coefficient (PCC), dynamic architecture, down-sampling, and specific state resetting and weight initialization. The results demonstrate the efficacy of our proposed model, as indicated by low root mean square error (RMSE) and mean bias error (MBE) values. For zone temperature, the average RMSE and MBE are 0.3 °C and 0.18 °C (summer) and 0.5 °C and 0.2 °C (winter), respectively. Furthermore, the average RMSE and MBE for thermal load predictions are 12 W/m2 and -2.4 W/m2 (winter) and 10 W/m2 and -0.6 W/m2 (summer), respectively. These performance metrics establish our model as a valuable tool for optimizing heating and cooling systems, resulting in energy savings and cost reductions. Our findings emphasize the potential of ANNs for precise thermal load and indoor temperature predictions, offering practical implications for building operators, engineers, and researchers involved in energy management.

DOI Fonte

DOI: 10.1016/j.jobe.2023.107464

Journal article2020

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

Buffa S, Soppelsa A, Pipiciello M, Henze G, Fedrizzi R

Anton Soppelsa, Mauro Pipiciello

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

DOI Fonte

DOI: 10.3390/en13174339

Journal article2019

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

Bettoni D, Soppelsa A, Fedrizzi R, del Toro Matamoros RM

Anton Soppelsa

This paper discusses the development of a coupled Q-learning/fuzzy control algorithm to be applied to the control of solar domestic hot water systems. The controller brings the benefit of showing performance in line with the best reference controllers without the need for devoting time to modelling and simulations to tune its parameters before deployment. The performance of the proposed control algorithm was analysed in detail concerning the input membership function defining the fuzzy controller. The algorithm was compared to four standard reference control cases using three performance figures: the seasonal performance factor of the solar collectors, the seasonal performance factor of the system and the number of on/off cycles of the primary circulator. The work shows that the reinforced learning controller can find the best performing fuzzy controller within a family of controllers. It also shows how to increase the speed of the learning process by loading the controller with partial pre-existing information. The new controller performed significantly better than the best reference case with regard to the collectors’ performance factor (between 15% and 115%), and at the same time, to the number of on/off cycles of the primary circulator (1.2 per day down from 30 per day). Regarding the domestic hot water performance factor, the new controller performed about 11% worse than the best reference controller but greatly improved its on/off cycle figure (425 from 11,046). The decrease in performance was due to the choice of reward function, which was not selected for that purpose and it was blind to some of the factors influencing the system performance factor_.

DOI Fonte

DOI: 10.3390/asi2020015

Journal article2017

Development of a new dynamic test procedure for the laboratory characterization of a whole heating and cooling system

Menegon D, Soppelsa A, Fedrizzi R

Diego Menegon, Anton Soppelsa

The performance of heating and cooling systems are affected by their dynamic operating conditions. As a consequence, a sound evaluation of system performance should be done through a dynamic test procedure. However, the complexity, the cost, and the time for such type of experimentation are obstacles to the diffusion of this test method.

A new whole system test procedure was developed to reduce its potential failure. The test method considers the emulation of system components that cannot be installed in the laboratory, while the selection of short-yet-significant test sequence is performed classifying the days of the year with clustering analysis.

The procedure was applied to a solar-assisted heat pump system in four different European climates. The seasonal performance figures are extrapolated from the test results and compared with the numerical simulations of the entire heating and cooling system. In all test cases the seasonal performance factors are lower than the simulated ones by approximately 10%. The results obtained with dynamic tests confirm the necessity of their implementation, since they provide useful information to improve the system layout and control. In this way, the manufacturers can introduce more efficient products into the market.

DOI Fonte

DOI: 10.1016/j.apenergy.2017.08.120

Technical
Questi cookie sono necessari per il funzionamento del sito e non possono essere disattivati.

Service	Disattivo/Attivo	Terms Link
Youtube		https://support.google.com/youtube/answer/7671399?...
Vimeo		https://vimeo.com/terms
Flourish		https://flourish.studio/terms/
Datawrapper		https://www.datawrapper.de/terms
Power BI		https://www.microsoft.com/en/servicesagreement/
Facebook		https://www.facebook.com/policies/cookies/
Instagram		https://help.instagram.com/1896641480634370?ref=ig
X		https://help.twitter.com/en/rules-and-policies/twi...
Google Maps		https://maps.google.com/help/terms_maps/
LinkedIn		https://www.linkedin.com/legal/cookie-policy

Journal article2024

A dynamic programming based method for optimal control of a cascaded heat pump system with thermal energy storage

Diller T, Soppelsa A, Nagpal H, Fedrizzi R, Henze G

Himanshu Nagpal, Anton Soppelsa, Tim Christoph Diller

DOI Fonte

DOI: 10.1007/s11081-023-09853-5

Anton Soppelsa

About

Scientific Publications

A Two-Degree-of-Freedom Controller with Transport Delay Compensation for Application in Thermo-Hydraulic Circuits

An adaptive multi-seasonal ARIMA approach for domestic hot water load forecasting: A pilot study

On-Site temperature and irradiance forecast tuning for improved load prediction in buildings

A dynamic programming based method for optimal control of a cascaded heat pump system with thermal energy storage

Numerical and experimental investigation of stratified water storage tanks: An enhanced adaptive-grid model

The bidirectional substation for district heating users: experimental performance assessment with operational profiles of prosumer loads and distributed generation

Adaptive thermal load prediction in residential buildings using artificial neural networks

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

Development of a new dynamic test procedure for the laboratory characterization of a whole heating and cooling system

Science Shots Eurac Research Newsletter

Cosa facciamo

Dove siamo

Istituti & Center

Seguici

Preferenze Cookie

Contenuti esterni

Scientific Publications

A Two-Degree-of-Freedom Controller with Transport Delay Compensation for Application in Thermo-Hydraulic Circuits

An adaptive multi-seasonal ARIMA approach for domestic hot water load forecasting: A pilot study

On-Site temperature and irradiance forecast tuning for improved load prediction in buildings

A dynamic programming based method for optimal control of a cascaded heat pump system with thermal energy storage

Numerical and experimental investigation of stratified water storage tanks: An enhanced adaptive-grid model

The bidirectional substation for district heating users: experimental performance assessment with operational profiles of prosumer loads and distributed generation

Adaptive thermal load prediction in residential buildings using artificial neural networks

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

Development of a new dynamic test procedure for the laboratory characterization of a whole heating and cooling system