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Big Data in Energy Economics
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 cost–benefit 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.
Agent-Based Models of Energy Investment Decisions
This book demonstrates how bounded rational decision models can be standardized and parameterized by socio-economic data. Focusing on private energy technology investment decisions, the author shows how different representative agents can be constructed using search rules, analysis tools and decision strategies. Diffusion curves for energy technologies such as solar collectors, boilers and efficiency upgrades for buildings are calculated. Further, the model is extended to study the impact of firms’ competition on technology diffusion. The modeling approach presented in this book may serve as a template for applications in other domain.
