High quality data plays a pivotal role in business success across various dimensions. Accurate and reliable data empowers business leaders to make well informed decisions and achieve operational efficiency, promoting growth and profitability. Data quality encompasses more than just accuracy it also includes completeness, consistency, and relevance.
Energy forecasting plays a pivotal role in our modern world, where energy consumption, production and pricing are critical factors.
Energy forecasting involves predicting the demand load and price of various energy sources, including both fossil fuels and renewable energy resources like wind and solar.
With an accurate energy usage forecast, a business can efficiently allocate and manage resources, this is crucial to maintain a stable energy supply to the consumer; energy forecasting is fundamental as we transition to renewable energy sources which do not produce consistent energy. Energy companies, grid operators and industrial consumers rely on forecasts to optimise their operations. Over- or undercontracting can lead to significant financial losses, so precise forecasts are essential.
The Fourth Industrial Revolution, commonly known as IR4.0, has ushered in profound transformations in the landscape of industrial operations and manufacturing. Digital technologies and data derived from sensors are driving a wide array of innovations, spanning from advanced analytics and machine learning to the realms of augmented and virtual reality models.
Handling sensor-based data poses a unique challenge for conventional relational databases, which is why data historians were originally conceived in the latter part of the 1980s. They were specifically designed for integration with industrial automation systems like SCADA (supervisory control and data acquisition). Initially, their primary application was within the process manufacturing sector, encompassing industries such as oil and gas, chemicals, pharmaceuticals, pipelines, and refining.
This Historian system was developed as an ecosystem that provided a comprehensive solution, ranging from data interface software to data storage and data visualization. The industry 4.0 revolution has spurred automation in manufacturing, leveraging smart sensors and IoT devices to capture real-time data from the field. Furthermore, it has seen the increased utilization of Artificial Intelligence and Machine Learning for predictive analytics and decision support, both of which are data-hungry applications. So, with IR4.0 development what are the options available to cater requirements for real time data ingestion.
Generative AI is changing how we think about data, particularly the knowledge it can unlock from unstructured data that simply wasn't possible before. However, it's also peaked our curiosity about structured data - Can Generative AI also query structured data? Could it query time series data to answer questions such as:
What was the average actual power generated by Turbine 1 at ACME Wind Farm on 6 May?"
RTDIP has been built to simplify ingesting and querying time series data. One of the most anticipated features of the Real Time Data Ingestion Platform for 2023 is the ability to create streaming and batch ingestion pipelines according to requirements of the source of the data and needs of the data consumer. Of equal importance is the need to query this data and an article that focuses on egress will follow in due course.
Real Time Data Ingestion Platform leverages Delta and the concept of a Lakehouse to ingest, store and manage it's data. There are many benefits to Delta for performing data engineering tasks on files stored in a data lake including ACID transactions, maintenance, SQL query capability and performance at scale. To find out more about Delta Lakehouse please see here.