How can you collect and store energy data efficiently?

Identifying energy data sources involves auditing existing infrastructure, collaborating with stakeholders, embracing IoT devices for real-time data, reviewing utility bills, leveraging Building Management Systems, exploring external databases, prioritizing data quality, anticipating future integration needs, and maintaining a detailed inventory.

Identifying energy data sources is crucial step in collecting, storing, and analyzing data efficiently. Sometimes, when the energy data sources are not well-identified in the facility, it is a better idea to start with significant energy consumption areas. If the meters/sensors are already installed in significant energy use areas, data can be acquired and stored on local server/cloud, otherwise new sensors should be installed. Also, the granularity of the data plays an important role for the energy managers in their analysis, therefore, data should be stored at desired intervals.

Collecting and storing energy data efficiently is crucial for effective energy management and informed decision-making. Here are strategies to efficiently collect and store energy data: 1.Automated Data Collection 2.Smart Metering and Sensors 3.Data Logging and Time Stamping 4.Integration of Building Management Systems (BMS) 5.Cloud-Based Platforms 6.Historical Data Storage 7.Data Security Measures 8.Data Aggregation and Visualisation Tools 9.Real-Time Monitoring and Alerts 10.Energy Management Software 11.Collaboration with Utility Companies 12.Regular Data Quality Checks 13.Scalability Considerations

Choosing an effective data collection method for energy management involves key considerations: Define objectives, align frequency with analysis needs, evaluate technologies like IoT sensors, prioritize automation for error reduction, assess scalability and cost-effectiveness, ensure integration capabilities, prioritize data security, opt for user-friendly solutions, and verify compliance with industry standards.

Data cleaning and organization is essential step to draw useful insights from the collected data. Correlation analysis can help in identifying the outliers and potential errors in the data acquired using the sensors. When the datasets are larger in size, using programming language like Python (Pandas Library) can help in cleaning the data to get rid of duplicates and fill the missing values.

There is a more reliable way to organize data that doesn't require cleaning. Legend Energy Advisors developed a technology stack from the ground up called LegendAnalytics®. The LegendAnalytics® platform leverages cutting-edge technologies and methodologies to provide a comprehensive view of energy consumption patterns and optimization strategies. By harnessing data from various sources like smart meters, IoT devices, and grid infrastructure, Legend Analytics® empowers companies to make data-driven decisions, develop targeted strategies, and deliver optimized energy solutions to our clients. Oh...and it does it in real-time while feeding seamlessly into auditable reporting initiatives without cleaning data.

Choosing a software that maximizes data storage is wise! SkySpark by SkyFoundry uses about 1.5 bytes per sample average for a timestamp value pair whereas SQL uses about 8+ bytes. SkySpark compresses interval data into a smaller size thus accessing at a faster speed.