Data Processing Module - Building Management System - BMS

Data Processing Management

The Data Processing Module is one of the most critical components of the Building Management System (BMS) and converts the raw data obtained by the Data Collection Module into meaningful and usable information.

Data coming in continuously from devices and sensors in buildings can be difficult and complex to manage when processed in raw form. This module analyzes the data to identify energy consumption trends, detect anomalies, and generate insights to optimize building management processes.

This module is of vital importance, especially in large-scale buildings, to save energy, optimize maintenance processes and increase user comfort. Building managers can make quick decisions by interpreting complex data in a more understandable way and make operations more efficient thanks to the automatic analysis tools offered by the system.

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Data Processing Module

The Data Processing Module analyzes building data to provide managers with insights into energy consumption, anomaly detection, predictive maintenance, and device management. This increases building safety, efficiency, and sustainability, while saving energy.

Data Editing

Anomaly Detection

Trend Analysis

Optimization

Data Storage

Data Filtering and Cleaning

It analyzes and removes erroneous, incomplete or repetitive information from data collected from sensors and devices within the building. It prevents the production of erroneous analysis results due to incorrect data flow and increases analysis accuracy. The filtering process contributes to obtaining more reliable results in energy consumption and security analyses by cleaning noisy signals.

Features

Data Quality Control
Noise Reduction
Data Normalization

Data Quality Control:
Missing or inconsistent information in the data coming from the sensors is detected and filtered. In this way, incorrect results are prevented in the analysis processes and the reliability of the system is increased.

Noise Reduction
Advanced signal processing algorithms are applied to detect and clean noisy (abnormal) data streams. In this way, erroneous data, especially those originating from environmental factors, is eliminated and more accurate analysis is made possible.

Data Normalization:
Temperature, humidity or energy consumption data from different devices are converted into standard units and harmonized for analysis. This process ensures more accurate comparisons between systems and ensures consistent analysis results.

Anomaly Detection and Error Detection

It detects abnormalities by analyzing the situations in which the devices or energy consumption in the building deviate from normal. For example, if an HVAC device consumes more energy than expected or works for longer than normal, the system automatically warns. This feature allows early intervention, preventing malfunctions and reducing maintenance costs.

Features

Implementation of Anomaly Algorithms
Determination of Threshold Values
Event Log Recording

Application of Anomaly Algorithms
Expected device performance and energy consumption levels are analyzed to detect abnormal situations. The system identifies abnormal deviations by comparing with historical data and automatically sends notifications to the relevant units.

Determination of Threshold Values
Normal operating ranges are defined for each device and system. If these limits are exceeded, the system immediately notifies operators and ensures that preventive maintenance processes are initiated.

Event Logging
All detected anomalies and error conditions are recorded and forwarded to the reporting module. This allows analysis based on historical data and long-term system optimization.

Trend and Forecast Analysis

It makes predictions about possible future situations by identifying trends over time from the data collected. For example, it helps with resource planning by predicting how much energy the heating system will use in the winter or when maintenance will be required on devices.

Features

Analysis of Historical Data
Application of Forecast Models
Maintenance Planning

Analysis of Historical Data
Trends are determined by analyzing historical energy consumption data, temperature fluctuations and device operating times. In this way, data-driven decisions can be made for more efficient management of operational processes.

Application of Predictive Models
Using machine learning algorithms, HVAC systems’ energy consumption or the timing of equipment failures are predicted, thus optimizing resource utilization and preventing unexpected system failures.

Maintenance Planning
Maintenance requirements are determined in advance by analyzing the operating times and performance data of the devices. With planned maintenance processes, malfunctions are prevented, operational interruptions are minimized and maintenance costs are optimized.

Energy Consumption Optimization

It identifies unnecessary energy expenditures by analyzing the consumption of electricity, water, gas and other energy resources within the building. In this way, optimization opportunities for energy saving are identified, allowing building managers to continue their operations at lower costs.

Features

Energy Analysis and Reporting
Time Planning
Regional Energy Control

Energy Analysis and Reporting
Energy consumption levels within the building are continuously analyzed to identify inefficient systems. This way, energy savings can be achieved by determining which devices consume more energy than necessary.

Schedule
Define automatic scheduling rules to ensure that HVAC systems, lighting, and other devices operate only when needed, preventing unnecessary energy use and improving operational efficiency.

Regional Energy Control
Energy consumption is reduced in specific areas according to the occupancy rate within the building. By reducing energy consumption in unused or low-density areas, overall energy efficiency is increased and costs are optimized.

Data Visualization and Reporting

It allows building managers to interpret data more quickly and effectively by presenting the analysis results obtained in the form of graphs, tables and reports. With the reporting tools, weekly, monthly or annual analysis reports can be created to measure the efficiency of the system and plan improvement processes.

Features

Designing Dashboards
Incident and Alert Reports
Trend Charts and Heat Maps

Designing Dashboards
Energy consumption, temperature changes and device performance are visualized instantly thanks to user-friendly interfaces. Thus, managers can easily access critical data and make informed decisions.

Event and Warning Reports
Anomalies, energy consumption changes or security breaches are presented with detailed reports. In this way, potential problems can be detected in advance and timely intervention can be made.

Trend Graphs and Heat Maps
Temperature and energy usage patterns within the building are visualized with graphs. Thanks to heat maps, consumption levels in different areas can be analyzed and efficiency-enhancing measures can be taken.