TY  - GEN
AB  - This book combines energy economics and big data modeling analysis in energy conversion and management and comprehensively introduces the relevant theories, key technologies, and application examples of the smart energy economy. With the help of time series big data modeling results, energy economy managers develop reasonable and feasible pricing mechanisms of electricity price and improve the absorption capacity of the power grid. In addition, they also carry out scientific power equipment scheduling and costbenefit analysis according to the results of data mining, so as to avoid the loss caused by accidental damage of equipment. Energy users adjust their power consumption behavior through the modeling results provided and achieve the effect of energy saving and emission reduction while reasonably reducing the electricity expenditure. This book provides an important reference for professionals in related fields such as smart energy, smart economy, energy Internet, artificial intelligence, energy economics and policy.
AU  - Liu, Hui,
AU  - Nikitas, Nikolaos.
AU  - Li, Yanfei.
AU  - Yang, Rui.
CN  - HD9502.A2
CY  - Singapore :
DA  - 2022.
DO  - 10.1007/978-981-16-8965-9
DO  - doi
ID  - 1444361
KW  - Power resources
KW  - Big data.
KW  - Ressources énergétiques
KW  - Données volumineuses.
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-8965-9
N2  - This book combines energy economics and big data modeling analysis in energy conversion and management and comprehensively introduces the relevant theories, key technologies, and application examples of the smart energy economy. With the help of time series big data modeling results, energy economy managers develop reasonable and feasible pricing mechanisms of electricity price and improve the absorption capacity of the power grid. In addition, they also carry out scientific power equipment scheduling and costbenefit analysis according to the results of data mining, so as to avoid the loss caused by accidental damage of equipment. Energy users adjust their power consumption behavior through the modeling results provided and achieve the effect of energy saving and emission reduction while reasonably reducing the electricity expenditure. This book provides an important reference for professionals in related fields such as smart energy, smart economy, energy Internet, artificial intelligence, energy economics and policy.
PB  - Springer,
PP  - Singapore :
PY  - 2022.
SN  - 9789811689659
SN  - 9811689652
T1  - Big data in energy economics /
TI  - Big data in energy economics /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-8965-9
ER  -