Reshaping the energy landscape of Crete through renewable energy valleys.

Renewable energy valleys (REVs) represent a transformative concept poised to reshape global energy landscapes. These comprehensive ecosystems transition regions from conventional energy sources to sustainable, self-reliant hubs for renewable energy generation, distribution, and consumption. At their core, REVs integrate advanced information and communication technology (ICT), interoperable digital solutions, social innovation processes, and economically viable business models.

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

This article includes data collected from public and private buildings in Latvia as part of several projects/tenders funded from the governmental Climate Change Financial Instrument (KPFI) of Republic of Latvia. The data provided consists of information on 445 projects, the activities performed therein, as well as numerical data on CO emission and energy consumption before and after the projects' implementation. Data cover a period from 2011 to 2020 for various types of buildings.

Transfer learning strategies for solar power forecasting under data scarcity.

Accurately forecasting solar plants production is critical for balancing supply and demand and for scheduling distribution networks operation in the context of inclusive smart cities and energy communities. However, the problem becomes more demanding, when there is insufficient amount of data to adequately train forecasting models, due to plants being recently installed or because of lack of smart-meters. Transfer learning (TL) offers the capability of transferring knowledge from the source domain to different target domains to resolve related problems.