Also, according to the US energy information agency, the energy consumption of commercial buildings is increasing by 2.7% every year in India. Due to the lack of proper energy monitoring systems, most of the energy consumption in buildings goes to waste. In data analysis some factors like load factor, unbalance factor, rise time, high load duration period are calculated from the obtained load curve.
These parameters help the building operator or manager to properly utilize the energy consumption of a commercial building. Because commercial buildings are one of the main contributors to CO2 emissions, the use of ICT technology to monitor buildings' energy consumption is important [1]. In [2], a low-cost solution for energy monitoring and data analysis on the energy consumption of a commercial building is proposed.
Also, the advantages of energy monitoring in a commercial building are explained by introducing calculations of some of the factors such as load factor, current unbalance factor, rise time, fall time and peak load duration. In this, all multi-functional meters are each connected to the building's lighting distribution. These meters support RS485 communication which is used to collect energy consumption data to perform energy monitoring.
In the third, the collected data from the meter is preprocessed, followed by the creation of the database to store the preprocessed data is explained.
Multi Functional Meter
Communication Interface Details
The first section describes the multifunction meter and the meter's Slave ID settings and baud rate settings. This communication with the meter involves sending commands to the meter to read and write the meter's specific register. The meter can be addressed with a specific user-defined meter address (Slave ID) from 1 to 247.
Communication Parameters Setting
Data Retrieval
The connection between the meter and the FTDI-based USB to RS485 converter is shown in TABLE 2.2. Once the connection is complete, connect the FTDI-based USB to RS485 converter to the RaspberryPi. The counter is accessed by the RaspberryPi using the counter's child ID and communication parameters such as baudrate, parity.
The registers corresponding to the required data can be accessed through some functions with input parameters such as the function code and the register addresses. To read data from the meter, the function 'read registers(R1,R2,function code)' can be used, where R1 is the start register, R2 is the end register, the function code tells whether these registers are read-only or writable. When data is collected from reading registers, time and date are added to the data so that it can be easily distinguished from the previously collected data.
Data monitoring is continuous at all times, so a storage device (computer) is used to store the collected data. Thus, the data is sent to the computer for storage in the form of a text file or a csv file.
Data Preprocess
The first consists of the date of the data collected, the second consists of the time of the data collected and the last consists of the measured data itself. But only the values contained in the odd numbered indices are the actual instantaneous parameter values. The odd numbered indices are only considered and stored in a new array, to remove the unnecessary values.
So this new array each parameter value is obtained and converted to binary format of length 16 bit. In this binary number, if the 16th bit (MSB) is 1, it means that the number is in 2's complement form. Once the augmentation of the data is complete, the entire data is in integer format.
However, the actual value of each parameter can be obtained by multiplying by the multiplication factors corresponding to each parameter, as shown in TABLE.2.3. The figure clearly shows that not only the date and time, but also the measured data are separated into 16 columns, where each column corresponds to the actual current parameter.
Database Creation
Here the database is created for 3 meters, each has a corresponding table format as shown in Fig.2.4. Through this interface we can get the plot of a particular parameter for a particular gauge with a given time period and date.
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Case Study
The data can be analyzed by plotting the load curve over a day or a week or a month. Many load parameters like Near peak load, Near base load, Maximum demand, rise time, high load duration etc. One of the advantages by analyzing the load curve on commercial academic building is the preparation of timetable for scheduling classes and laboratory planning is possible based on the load consumption.
In the first section, an HTML interface is created with python to view interactive graphs of instantaneous parameters such as voltage, current, etc. In the second, the factors used for the load curve analysis are explained and the case study on the actual load curve is also made.
HTML interface for interactive plots
It is clear from the figure that smaller amounts of power are consumed during the night until 08:30, when all lights will be turned off. And from 08:30 to 17:00, more power is consumed, and then the power consumption slowly decreases. Other parameters like average voltage, average current, total active power, total power factor etc. can also be plotted.
From the power factor graph we can conclude how much power the load consumes in a day, week or month.
Load Curve Data Analysis
Case study and Results
The load curve shows a high ramp in the morning, so the rise time is high. The main objective is to interact the meter with the Arduino using external buttons to configure the meter's communication parameters. Using these buttons, the Slave ID of the meter can be configured from 1 to 247. So for a single RaspberryPi, about 247 meters are connected.
Configuring Arduino communication parameters
And it is more useful if the Baud Rate of the meter is selectable on demand basis. The first is to warn the worker about the overload of the cable corresponding to the phase. Once all the gauges are connected to the RaspberryPi, there is no need to constantly turn off the power for this experiment.
Also, the advantages of energy monitoring in a commercial building are explained by introducing calculations of some of the factors such as load factor, unbalance factor, rise time and peak load duration period. Interfacing the meter to the Arduino is also shown and can be expanded to take full control over the meter.
