TY - CHAP
T1 - Providing a Scientific Arm to Renewable Energy Cooperatives
AU - Chalkiadakis, Georgios
AU - Akasiadis, Charilaos
AU - Savvakis, Nikolaos
AU - Tsoutsos, Theocharis
AU - Hoppe, Thomas
AU - Coenen, Frans
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018/7
Y1 - 2018/7
N2 - Renewable Energy-Supplying cooperatives (REScoops) are cooperatives of renewable energy producers and/or consumers, which are under formulation in the emerging European smart grid. Their emergence highlights the importance of proconsuming green energy and simultaneously puts forward principles such as energy democracy and self-consumption, assists the fight against energy poverty, and helps reduce GHG emissions. To this end, the incorporation of scientific and technological solutions into the REScoops’ everyday business and practices, is key for improving these practices and assessing their potential benefits, and as such for enabling them to deliver the maximum possible gains to their members and society at large. This chapter outlines three key axes of scientific research and solutions that can be used for REScoops, namely, (a) a statistical analysis, (b) an applied behavioural analysis, and (c) an artificial intelligence/machine learning one. Also presented are results and lessons learned from providing such solutions to European REScoops as part of the H2020 REScoop Plus project.
AB - Renewable Energy-Supplying cooperatives (REScoops) are cooperatives of renewable energy producers and/or consumers, which are under formulation in the emerging European smart grid. Their emergence highlights the importance of proconsuming green energy and simultaneously puts forward principles such as energy democracy and self-consumption, assists the fight against energy poverty, and helps reduce GHG emissions. To this end, the incorporation of scientific and technological solutions into the REScoops’ everyday business and practices, is key for improving these practices and assessing their potential benefits, and as such for enabling them to deliver the maximum possible gains to their members and society at large. This chapter outlines three key axes of scientific research and solutions that can be used for REScoops, namely, (a) a statistical analysis, (b) an applied behavioural analysis, and (c) an artificial intelligence/machine learning one. Also presented are results and lessons learned from providing such solutions to European REScoops as part of the H2020 REScoop Plus project.
KW - 2022 OA procedure
KW - Renewable energy sources cooperative (REScoop)
KW - Behavioural analysis
KW - Statistical analysis
KW - Demand-side management
KW - Smart grid
U2 - 10.1007/978-3-319-89845-2_51
DO - 10.1007/978-3-319-89845-2_51
M3 - Chapter
SN - 978-3-319-89844-5
T3 - Green Energy and Technology
SP - 717
EP - 731
BT - The Role of Exergy in Energy and the Environment
A2 - Nižetić, Sandro
A2 - Papadopoulos , Agis
PB - Springer
CY - Cham
ER -
