|
The use of computer tools and applications in the process of achieving
electricity economy enables efficient management of the technical infrastructure
potential and elements of building equipment. The presented software tools do not
eliminate the human factor from the supervision process, but they do facilitate
the work and expand its capabilities. Thanks to the tools enabling the management
of the electricity distribution process, the information transfer process and
the decision-making process can be improved and shortening the time
of identifying a potential threat by the BMS system also reduces the negative
economic effects (cost reduction) and ecological failures (limiting the emission
of hazardous compounds).
Thanks to the use of software enabling the acquisition and analysis
of measurement data, it is possible to change the strategy of using individual
systems and, as a result, reduce the demand for electricity in buildings. The use
of analytical programs in one of the facilities of Lublin University of Technology
made it possible to reduce the demand for electricity by approx. 20% compared
to the reference period in which the building was operated with the manufacturer’s
standard settings. This results in a reduction of electricity consumption by over
250,000 kWh per year.
The use of software for the acquisition and analysis of measurement data
allows simulations related to the selection of electricity tariffs and their adaptation
to the nature of the building load. Additionally, it is possible to analyse other
technical parameters, such as reactive power consumption. Thanks to the analysis
of reactive power data and the use of reactive power compensation systems, its
share in electricity bills has been limited to trace amounts.
The analysis of time histories and the knowledge of the functioning
of the technical infrastructure in the building enables the reduction of daily and
monthly power peaks while the demand for electricity remains unchanged. Based
on the measurement data, the determination of correspondingly lower expected
peak loads reduces the costs of electricity supply. An additional rational
distribution of the load variability course also reduces the charges related
to the capacity fee.
|
