A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks
Forecasting is an important task for intelligent agents involved in dynamical processes. A specific application domain concerns district heating networks, in which the future heating load generated by centralized power plants and distributed to buildings must be optimized for better plant maintenance, energy consumption and environmental impact. In this paper we present XM_HeatForecast a Python tool designed to support district heating network operators. The tool provides an integrated architecture for i) generating and updating in real-time predictive models of heating load, ii) supporting the analysis of prediction performance and errors, iii) inspecting model parameters and analyzing the historical dataset from which models are trained. A case study is presented in which the software is used on a synthetic dataset of heat loads and weather forecast from which a regression model is generated and updated every 24 h, while predictions of load in the next 48 h are performed every hour. Software available at: https://github.com/XModeling Video available at: https://youtu.be/JtInizI4e_s.
XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks
Forecasting is an important task for intelligent agents involved in dynamical processes. A specific application domain concerns district heating networks, in which the future heating load generated by centralized power plants and distributed to buildings must be optimized for better plant maintenance, energy consumption and environmental impact. In this paper we present XM_HeatForecast a Python tool designed to support district heating network operators. The tool provides an integrated architecture for i) generating and updating in real-time predictive models of heating load, ii) supporting the analysis of prediction performance and errors, iii) inspecting model parameters and analyzing the historical dataset from which models are trained. A case study is presented in which the software is used on a synthetic dataset of heat loads and weather forecast from which a regression model is generated and updated every 24 h, while predictions of load in the next 48 h are performed every hour. Software available at: https://github.com/XModeling Video available at: https://youtu.be/JtInizI4e_s.
XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks
Federico Bianchi (author) / Francesco Masillo (author) / Alberto Castellini (author) / Alessandro Farinelli (author) / Bianchi, Federico / Masillo, Francesco / Castellini, Alberto / Farinelli, Alessandro
2021-01-01
Conference paper
Electronic Resource
English
DDC:
690
Forecasting Heat Load for Smart District Heating Systems : A Machine Learning Approach
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Load management in district heating systems
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