Harnessing machine learning algorithms to unveil energy efficiency investment archetypes
Authors:
- Diamantis KoutsandreasDKDiamantis Koutsandreas
- Ilkka KeppoIKIlkka Keppo
Open Access
- 11 September 2024
- journal article
- research article
- Published by Elsevier in Energy Reports
- Vol. 12, 3180-3195
Abstract
No abstract availableKeywords
This publication has 38 references indexed in Scilit:
- The energy efficiency gap and barriers to investments: Evidence from a firm survey in The NetherlandsEnergy Economics, 2024
- Optimization design of short-circuit test platform for the distribution network of integrated power system based on improved K-means clusteringEnergy Reports, 2023
- Bridging the transparency gap in energy efficiency financing by co-designing an integrated assessment framework with involved actorsEnergy Reports, 2022
- Risks and mitigation strategies in energy efficiency financing: A systematic literature reviewEnergy Reports, 2022
- A meta-learning classification model for supporting decisions on energy efficiency investmentsEnergy and Buildings, 2022
- Using clustering algorithms to characterise uncertain long-term decarbonisation pathwaysApplied Energy, 2020
- Pattern recognition in building energy performance over time using energy benchmarking dataApplied Energy, 2018
- An application of normative decision theory to the valuation of energy efficiency investments under uncertaintyAutomation in Construction, 2017
- Application of clustering for the development of retrofit strategies for large building stocksAdvanced Engineering Informatics, 2017
- A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares PartitioningPsychometrika, 2007