Abstract: Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.
Abstract: We regard forecasting of energy consumption by
private production areas of a large industrial facility as well as by the
facility itself. As for production areas, the forecast is made based on
empirical dependencies of the specific energy consumption and the
production output. As for the facility itself, implementation of the
task to minimize the energy consumption forecasting error is based
on adjustment of the facility’s actual energy consumption values
evaluated with the metering device and the total design energy
consumption of separate production areas of the facility. The
suggested procedure of optimal energy consumption was tested based
on the actual data of core product output and energy consumption by
a group of workshops and power plants of the large iron and steel
facility. Test results show that implementation of this procedure gives
the mean accuracy of energy consumption forecasting for winter
2014 of 0.11% for the group of workshops and 0.137% for the power
plants.