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 …

Forecasting heat load for smart district heating systems: A machine learning approach

S Idowu, S Saguna, C Åhlund… - 2014 IEEE international …, 2014 - ieeexplore.ieee.org
The rapid increase in energy demand requires effective measures to plan and optimize
resources for efficient energy production within a smart grid environment. This paper …

Improved heat demand prediction of individual households

V Bakker, MGC Bosman, A Molderink, JL Hurink… - IFAC Proceedings …, 2010 - Elsevier
One of the options to increase the energy efficiency of current electricity network is the use of
a Virtual Power Plant. By using multiple small (micro) generators distributed over the …

[PDF][PDF] Comparison of Machine Learning Approaches for Forecasting Building Heat Load in DHS

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

Energy load forecasting model for integrated urban energy planning

E Jaraminienė, D Biekša - 2011 - etalpykla.vilniustech.lt
Nearly 75% of the world's energy is consumed in urban areas and it is expected that
together with the cities expansion it will grow rapidly in the future. Therefore the energy …

Applied Machine Learning in District Heating System

SO Idowu - 2018 - diva-portal.org
In an increasingly applied domain of pervasive computing, sensing devices are being
deployed progressively for data acquisition from various systems through the use of …