Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings

Romańska, Anna; Dudzik, Marek; Dudek, Piotr; Górny, Mariusz; Kuc, Sabina; Bomberg, Mark

doi:10.3390/en19102446

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Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings

by

Anna Romańska

^1,*,

Marek Dudzik

¹,

Piotr Dudek

²

,

Mariusz Górny

²,

Sabina Kuc

³ and

Mark Bomberg

⁴

¹

Department of Infotronics and Cybersecurity, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland

²

AI4Smart SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ, ul. Pietrusińskiego 6/3, 30-222 Krakow, Poland

³

Department of Architectural and Construction Design, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland

⁴

Department of Mechanical and Aeronautical Engineering, Wallace H. Coulter School of Engineering, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699-5725, USA

^*

Author to whom correspondence should be addressed.

Energies 2026, 19(10), 2446; https://doi.org/10.3390/en19102446

Submission received: 10 November 2025 / Revised: 16 March 2026 / Accepted: 24 March 2026 / Published: 19 May 2026

(This article belongs to the Special Issue Science and Practice of Energy Technology in Residential Buildings)

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Abstract

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use.

Keywords: buildings energy consumption model; LSTM; AI; LSTM optimization; automatic control systems

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MDPI and ACS Style

Romańska, A.; Dudzik, M.; Dudek, P.; Górny, M.; Kuc, S.; Bomberg, M. Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings. Energies 2026, 19, 2446. https://doi.org/10.3390/en19102446

AMA Style

Romańska A, Dudzik M, Dudek P, Górny M, Kuc S, Bomberg M. Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings. Energies. 2026; 19(10):2446. https://doi.org/10.3390/en19102446

Chicago/Turabian Style

Romańska, Anna, Marek Dudzik, Piotr Dudek, Mariusz Górny, Sabina Kuc, and Mark Bomberg. 2026. "Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings" Energies 19, no. 10: 2446. https://doi.org/10.3390/en19102446

APA Style

Romańska, A., Dudzik, M., Dudek, P., Górny, M., Kuc, S., & Bomberg, M. (2026). Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings. Energies, 19(10), 2446. https://doi.org/10.3390/en19102446

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Perspective for Improving Energy Efficiency and Indoor Climate Towards Prediction of Energy Use: A Generalized LSTM-Based Model for Non-Residential Buildings

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