PEOPLEHimanshu Nagpal

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Himanshu Nagpal

Senior Researcher
Institute for Renewable Energy

T +39 0471 055 613

himanshu.nagpal@eurac.edu

https://orcid.org/0000-0001-8547-9556

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About

My primary research interest is in the modelling, control, and simulation of building energy systems. Specifically, I work on model predictive control strategies for the optimal operation of HVAC systems. Additionally, taking advantage of my interdisciplinary background in mathematics and engineering, I have conducted research in various other fields also, such as traffic flow modelling, time-series forecasting, and wastewater heat recovery.

Google scholar:
https://scholar.google.com/citations?user=igB4QJ4AAAAJ&hl=en

Scientific Publications

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

Project: EU H2020 HAPPENING: Heat pumps in existing multi-family buildings for achieving union's energy and environmental goals

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 Source

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

Project: EU H2020 HAPPENING: Heat pumps in existing multi-family buildings for achieving union's energy and environmental goals

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 Source

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 Source

DOI: 10.1007/s11081-023-09853-5

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

Project: EU H2020 REWARDHeat - Renewable and Waste Heat Recovery for Competitive District Heating and Cooling Networks

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 Source

DOI: 10.1016/j.jobe.2023.107464

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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 Source

DOI: 10.1007/s11081-023-09853-5

Himanshu Nagpal

About

Scientific Publications

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

Adaptive thermal load prediction in residential buildings using artificial neural networks

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Scientific Publications

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

Adaptive thermal load prediction in residential buildings using artificial neural networks