Big Data Issues in Electricity Measurement

Electrical measurement involves the collection of data on various parameters such as voltage, current, and power in electrical systems. Big data refers to the large and complex sets of data that are generated in various industries, including the energy sector.

The use of big data analytics in electrical measurement can provide insights into energy usage patterns, identify areas of inefficiency or waste, and optimize energy consumption. This can lead to improved energy efficiency, reduced costs, and a more sustainable energy system.

Advanced analytics techniques such as machine learning can be used to analyze large amounts of data in real-time and provide actionable insights, enabling energy managers and operators to make data-driven decisions and improve the performance of electrical systems. However, the implementation of big data analytics in electrical measurement requires careful consideration of data security, privacy, and quality issues.

What is a value for money and significance?

Big volume of data can make it difficult to manage and analyze effectively. Big data analytics requires specialized tools and expertise to manage and analyze large amounts of data efficiently. Organizations must also ensure that they have the necessary resources and infrastructure to support the collection, storage, and analysis of large amounts of data.

The challenge with collecting large amounts of data, such as the 10MB of data produced by full speed BMC data readings in a 5 minute period, is determining whether the data is valuable and significant. Collecting large amounts of data can be useful in identifying patterns and trends, but it can also make it difficult to identify relevant information.

Another challenge is the processing power and storage capacity required to manage and analyze large amounts of data. Collecting and storing large amounts of data can be expensive, and it requires specialized tools and expertise to manage and analyze effectively.

Advanced methods for optimization

More data is better!
Despite these challenges, the collection and analysis of large amounts of data can be highly valuable in improving performance and identifying areas for optimization. Advanced analytics techniques, such as machine learning and artificial intelligence, can be used to identify patterns and trends in the data, providing valuable insights that can inform decision-making and improve performance

Significance of fragment data
There is summary of power. But power is a sum of fragments about current, voltage and phase angle.

15 minutes problem
If data is an sum of measurement in 15 minutes period it presents an average consumption. The variance of measurements disappears like dust in the wind.

Big question is what kind of data or information lost?
Solutions is an compromise, maybe?