Abstract: Energy consumption data, in particular those involving
public buildings, are impacted by many factors: the building structure,
climate/environmental parameters, construction, system operating
condition, and user behavior patterns. Traditional methods for data
analysis are insufficient. This paper delves into the data mining
technology to determine its application in the analysis of building
energy consumption data including energy consumption prediction,
fault diagnosis, and optimal operation. Recent literature are reviewed
and summarized, the problems faced by data mining technology in the
area of energy consumption data analysis are enumerated, and research
points for future studies are given.
Abstract: In this study, the performance analyses of the twenty
five Coal-Fired Power Plants (CFPPs) used for electricity generation
are carried out through various Data Envelopment Analysis (DEA)
models. Three efficiency indices are defined and pursued. During the
calculation of the operational performance, energy and non-energy
variables are used as input, and net electricity produced is used as
desired output (Model-1). CO2 emitted to the environment is used as
the undesired output (Model-2) in the computation of the pure
environmental performance while in Model-3 CO2 emissions is
considered as detrimental input in the calculation of operational and
environmental performance. Empirical results show that most of the
plants are operating in increasing returns to scale region and Mettur
plant is efficient one with regards to energy use and environment.
The result also indicates that the undesirable output effect is
insignificant in the research sample. The present study will provide
clues to plant operators towards raising the operational and
environmental performance of CFPPs.