A hybrid machine learning approach for the load prediction in the sustainable transition of district heating networks

This publication has 28 references indexed in Scilit:

• Clustering-based analysis for residential district heating data
  Energy Conversion and Management, 2018
• Utilizing data center waste heat in district heating – Impacts on energy efficiency and prospects for low-temperature district heating networks
  Energy, 2017
• International review of district heating and cooling
  Energy, 2017
• An Analyze of Long-term Hourly District Heat Demand Forecasting of a Commercial Building Using Neural Networks
  Energy Procedia, 2017
• Behavior Change of Building Users and Energy Consumption
  2017
• Time-series clustering – A decade review
  Information Systems, 2015
• 4th Generation District Heating (4GDH)
  Energy, 2014
• Efficient technologies or user behaviour, which is the more important when reducing households’ energy consumption?
  Energy Efficiency, 2012
• A review on buildings energy consumption information
  Energy and Buildings, 2008
• Modelling the heat consumption in district heating systems using a grey-box approach
  Energy and Buildings, 2006