We discuss time-series data on our website and hear it mentioned quite a bit in the energy industry. But what exactly is it? This blog will explore what time-series data means and provide examples of how it is pertinent to utilities.
This type of data that is collected and recorded over a sequence of (usually) equally-spaced time intervals. It involves a series of data points ordered by time, where each data point corresponds to a specific period or timestamp and may have multiple quantities (measurements, metrics) associated with it. Depending on the application, time series data can be recorded at various frequencies, such as hourly, daily, weekly, monthly, or even in finer or coarser intervals. It is transmitted to servers for storage immediately for each data point or in less-frequent batches, each with multiple data points.
In the context of utilities and the energy industry, this type of data is extensively used for monitoring and managing various aspects of utility operations.
Here are some everyday use cases of time series in utilities:
- Energy Consumption Monitoring: Utility companies use time series data (typically coming from meters) to track the energy consumption patterns of their customers over time. This helps understand peak demand periods, identify energy-saving opportunities, and optimize power generation and distribution.
- Load Forecasting: Historic consumption time series data forecasts future electricity demand. Accurate load forecasting helps utilities plan their power generation and distribution strategies, ensuring enough electricity is generated to meet the expected demand without overloading the grid.
- Renewable Energy Integration: For utilities that utilize renewable energy sources like solar or wind power, the time series of electricity generated by the renewables are crucial in understanding the intermittent nature of these energy sources. This information aids in optimizing energy storage, managing grid stability, and ensuring a reliable power supply.
- Demand Response Management: Time series data plays a significant role in demand response programs, where utilities encourage customers to adjust their energy consumption during peak periods. Utilities can engage customers and balance energy demand during critical times by providing real-time or near-real-time data.
- Price Forecasting: Time series data is used to predict electricity prices. This is particularly important in liberalized energy markets where prices fluctuate based on supply and demand conditions.
For more uses of time series data, feel free to go to the Awesense Use Case library and get inspired about what additional value utilities can get out of time series.
Time Series Challenges
Despite its many applications, time series can be challenging. Some of the challenges associated with working with this type of data are the following:
- Irregular Sampling: Time series data may not always be uniformly sampled, and there can be missing data or irregular time intervals. Handling such irregularities and gaps requires careful preprocessing and interpolation techniques to maintain data integrity.
- Noise and outliers: Time series data can be noisy, containing random variations that obscure underlying patterns. Additionally, outliers or anomalies may occur due to exceptional events. Robust methods are needed to distinguish between meaningful patterns and random fluctuations or anomalies.
- Missing reference element: Time series can be referring (be attached) to a non-existent element (e.g. in the GIS data), preventing from precisely locating the true real-world origin of the measured data.
Beyond the challenges with the actual time-series data, the issue of collating and streamlining time-series data with other data sets to provide context can be challenging.
- Data Format and Structure: Different datasets may have varying formats, structures, and granularities, making it challenging to integrate them seamlessly. Datasets from different sources may have distinct semantics, units, or measurement scales. Understanding these domain-specific differences and transforming the data accordingly is essential for meaningful integration. Standardizing the data formats and ensuring consistency is vital for proper integration. Properly linking and joining data from different sources requires identifying standard identifiers or keys (see the point above about missing reference elements). Additionally, formats can change over time, and ensuring proper version control and updating procedures are crucial to avoid working with outdated or incompatible data. The entire process can be complex when dealing with data from diverse domains.
- Time Synchronization: Ensuring that the timestamps of various time series datasets align correctly is crucial. Mismatches in time zones, different resolutions, daylight saving time observation, or irregular sampling intervals can complicate the alignment process. Merging time series data with other datasets often requires identifying relevant features that can provide valuable context.
- Data Volume: Handling large volumes of time series data and other datasets can strain computational resources and may necessitate data reduction techniques or distributed computing. Datasets from various sources may evolve, leading to versioning issues. In addition, as the number of integrated datasets grows, the computational complexity of processing and analyzing the combined data increases significantly.
Harnessing the power of time series is a crucial step toward optimizing efficiency and delivering reliable, sustainable energy. However, navigating the complexities of this data type can take time and effort. This is where Awesense steps in with its revolutionary AI Data Engine. The engine uses many sophisticated computational techniques to address the complexities of time series, enabling it to be structured according to our Energy Data Model (EDM) and other relevant data sets. The engine validates, estimates, and synchronizes the data according to time and space to create a digital twin of the grid. This digital twin is made easily accessible via APIs enabling the development of analytics, use cases, and modeling for grid optimization. Data is the tool that will drive grid decarbonization, and we are passionate about helping its digital transformation.
For more information on how we work with utilities to efficiently use their data with our AI Data Engine, contact us!