A data-driven multicriteria decision making tool for assessing investments in energy efficiency

Abstract

Mainstreaming energy efficiency financing has been considered a key priority during the last decade among several stakeholders. The capability offered by Multicriteria Decision Analysis to integrate cross-domain financial and energy consumption data, combined with statistical analysis techniques and data abundance, contributes to building the necessary market confidence in energy efficiency projects and make them an attractive investment asset class. In this context, the aim of this paper is to propose a solid methodological framework in order to support the financing procedure of energy efficiency investments, and to identify improved grant financing plans, considering a series of factors which are of vital importance for the sustainability of such actions and the limitation of investment risk. A decision support tool, developed in Python, is presented which implements the suggested methodology, improving the decision making for the investor in terms of the percentage of grant financing per project. The developed methodology has been applied on a reliable dataset of energy efficiency projects from several cities in Latvia, where the actual performance of the investments is exploited. The application of the methodology has resulted in a financing plan which achieves about the same energy savings, while bringing 15% reduction of the energy efficiency investments’ cost.

Improving energy performance of buildings: Dataset of implemented energy efficiency renovation projects in Latvia

“… Data provide valuable information on the implementation of funded projects for the improvement of buildings’ energy efficiency [2] . They can be relevant for the evaluation of a variety of activities taken place in different types of buildings, aiming at the reduction of the buildings’ CO 2 emission and energy consumption [3] . Researchers, policy makers, as well as professionals working for the improvement of energy performance in buildings [4 , 5] may benefit from the data collected under the framework of the KPFI government programme.…”

“…Data provide valuable information on the implementation of funded projects for the improvement of buildings’ energy efficiency [2] . They can be relevant for the evaluation of a variety of activities taken place in different types of buildings, aiming at the reduction of the buildings’ CO 2 emission and energy consumption [3] .…”

An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids

“…Buildings account for 40% of the global energy consumption and greenhouse gas (GHG) emissions, giving them a pivotal role in the recent climate crisis and global warming [30,31]. Thus, the ability to predict the electrical consumption of a building or a specific area of the building is extremely useful in the context of the effort made to increase energy efficiency.…”

Optimal site selection of electric vehicle charging stations exploiting multi-criteria decision analysis: The case of Greek municipalities

“…Due to the multi-dimensional nature of the problem, the proposed approach builds on a multi-criteria decision analysis (MCDA) method that effectively filters the alternatives and identifies the optimal solution such that no other feasible option exists that is equally good to the selected solution [6]. Apart from considering trade-offs among the various criteria defined, the proposed approach can also incorporate the judgment of decision makers (DMs), experts, and stakeholders (estimation of criteria weights), while also accounting for uncertainty (evaluation of criteria using qualitative measures) [7]. The rest of the paper is structured as follows: Section 2 includes a presentation of past studies on the problem of optimal EVCS placement.…”