Applied machine learning: Forecasting heat load in district heating system

S Idowu, S Saguna, C Åhlund, O Schelén - Energy and Buildings, 2016 - Elsevier
Forecasting energy consumption in buildings is a key step towards the realization of
optimized energy production, distribution and consumption. This paper presents a data …

[HTML][HTML] Operational thermal load forecasting in district heating networks using machine learning and expert advice

D Geysen, O De Somer, C Johansson, J Brage… - Energy and …, 2018 - Elsevier
Forecasting thermal load is a key component for the majority of optimization solutions for
controlling district heating and cooling systems. Recent studies have analysed the results of …

A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system

J Runge, E Saloux - Energy, 2023 - Elsevier
Forecasting the short-term future energy demand in buildings and districts is a vital
component towards the optimization of energy use and consequently the reduction in …

Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings

A Moradzadeh, A Mansour-Saatloo… - Applied Sciences, 2020 - mdpi.com
Nowadays, since energy management of buildings contributes to the operation cost, many
efforts are made to optimize the energy consumption of buildings. In addition, the most …

Heat demand forecasting algorithm for a Warsaw district heating network

T Kurek, A Bielecki, K Świrski, K Wojdan, M Guzek… - Energy, 2021 - Elsevier
This paper presents a complex analysis of heat demand forecasting methods for the Warsaw
District Heating Network, which is owned by Veolia Energia Warszawa, the largest district …

Forecasting district heating demand using machine learning algorithms

E Saloux, JA Candanedo - Energy Procedia, 2018 - Elsevier
Short-term forecasting of thermal energy demand is critical to optimally manage on-site
renewable energy generation and the charge and discharge of energy storage devices in …

Prediction of residential district heating load based on machine learning: A case study

Z Wei, T Zhang, B Yue, Y Ding, R Xiao, R Wang, X Zhai - Energy, 2021 - Elsevier
Heating load prediction plays an important role in supporting the operation of a residential
district energy station. To find out the most suitable prediction algorithm, seven popular …

A review on time series forecasting techniques for building energy consumption

C Deb, F Zhang, J Yang, SE Lee, KW Shah - Renewable and Sustainable …, 2017 - Elsevier
Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …

[HTML][HTML] Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters

M Lumbreras, R Garay-Martinez, B Arregi… - Energy, 2022 - Elsevier
An accurate characterization and prediction of heat loads in buildings connected to a District
Heating (DH) network is crucial for the effective operation of these systems. The high …

Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A …

D Koschwitz, J Frisch, C Van Treeck - Energy, 2018 - Elsevier
Predicting building energy consumption is essential for planning and managing energy
systems. In recent times, numerous studies focus on load forecasting models dealing with a …