Today’s utilization of machine learning (ML) in the energy space spans a variety of applications, such as demand forecasting, asset management, renewable energy integration, and customer engagement. Effective algorithms help predict electricity, gas, and water usage patterns for efficient grid management and resource allocation. Subsequent industry benefits include optimizing maintenance schedules for generators, power lines, and other infrastructure based on sensor data and predictive models. Customers can benefit from such perks as personalized energy bills, targeted energy-saving recommendations, and chatbots and virtual assistants that can expedite customer service. While ML adoption within the industry was initially a bit slow due to data silos, lack of skilled personnel, and concerns about model explainability, the technology has evolved in recent years through advancements related to data management, cloud computing, and open-source libraries.
ML models rely on two key components: data and algorithms. Algorithms are used to build and refine models, while data is crucial for training them. To improve model quality and business decision-making, it is critical for IT teams to enhance either their algorithms or their training data. Real-world data, however, tends to be more unstructured and messier when compared to pristine algorithm code. This makes data preparation and cleansing a more complex and time-consuming task. Low-code ML platforms can accelerate this process by enabling rapid data exploration and manipulation. They provide intuitive visual interfaces to try different data pre-processing steps and feature engineering pipelines that transform and enrich data, improving the performance of ML algorithms. This facilitates quick iteration to discover which data preparation works best for a given model and business problem. By empowering faster, iterative data wrangling, low-code tools can help improve model quality by enhancing the more disorganized and cumbersome data input. The hands-on, easy-to-use low code enables fewer technical domain experts to participate actively in the ML process.
Also, the emergence of data-centric artificial intelligence (AI) influences the integrity of ML as it relates to algorithms. This burgeoning science studies various techniques to improve datasets and the performance of ML applications. The use of data-centric AI will continue to grow within the energy industry.
Data-centric AI makes its mark
Data-centric AI is a concept coined in 2021 by Andrew Ng, a computer scientist widely credited with being a pioneer in areas related to ML and online education. It revolves around the idea that data is not just a resource but the core of AI systems. Data-centric AI flips the script from algorithm-centric approaches by prioritizing data governance and quality, data infrastructure, data-driven model development, and continuous data feedback. By establishing comprehensive data management practices to ensure reliable and accurate data sets, data-centric AI builds robust data platforms to integrate, store, and access data from various sources. ML models are tailored to specific problems using relevant data sets and domain expertise. Feedback loops are also employed to update and refine models based on new data and real-world performance.
Evaluating data quality
Data quality is paramount for effective ML models. The adage “garbage in, garbage out” applies here as low-quality data will lead to inaccurate and unreliable models. Factors influencing data quality include:
- Accuracy. Data should be free from errors and inconsistencies.
- Completeness. Missing data can bias models and lead to inaccurate predictions.
- Timeliness. Real-time or near-real-time data is crucial for applications such as demand forecasting and grid management.
- Relevancy. Data should be relevant to the specific problem the model is trying to solve.
There are a variety of methods that can be taken to assess the level of data quality. Data profiling takes the approach of analyzing data to understand its distribution, missing values, and outliers. Data can also be “visualized” through the creation of charts and graphs that can identify patterns and anomalies. Additionally, data cleansing allows for correcting errors, filling in missing values by identifying irrelevant parts of the data, and modifying any problematic data.
Another option for data assessment is to acquire domain expertise by leveraging the knowledge of industry professionals to better understand and interpret data.
Real-world data reflects the complexities and nuances of the energy sector, while algorithm code represents the mathematical logic used by ML models. Understanding this distinction is critical for model training, interpretability, and generalizability. Training data should accurately reflect real-world scenarios for models to be more transparent and understandable for all stakeholders. This helps to ensure that models perform well beyond the specific data they were trained on.
Significance of low-code platforms
Low-code platforms allow users with limited coding experience to build applications and automate tasks through visual interfaces and pre-built components. This facilitates faster data preparation and cleansing with the use of drag-and-drop tools, automated workflows, and pre-built connectors that streamline data transformation. Low-code platforms also offer improved accessibility by making data accessible to non-programmers in the energy sector, democratizing data science. Faster development cycles and lower maintenance requirements, when compared to traditional coding, result in reduced development costs and time when implemented by low-code platforms.
Low-code offers additional benefits such as building custom ML models, automating tasks, and improving collaboration. Rapidly prototyping and deploying ML models allow for specific needs to be accomplished without requiring extensive coding. Repetitive tasks, including data analysis and report generation, can be automated to free up personnel for higher-value activities. Similarly, cross-functional teams can be implemented to collaborate on data-driven projects.
A wide range of individuals will benefit from the use of low-code tools. Data analysts and scientists can focus on model development and analysis without getting overwhelmed with data preparation. Domain experts can leverage data to solve specific problems without learning complex coding. Business users can build dashboards and reports to track progress and make data-driven decisions.
The future of ML models
As technology continues to advance, there are numerous paths that the use of ML models can take moving forward. Although many challenges have impacted the rate of digitalization across the energy industry, especially for smaller companies, there are already successful integration examples. According to a recent report by Forbes, a Texas-based powerplant is part of a collaborative that has developed a neural network, a subset of ML that mimics how biological neurons in the human brain signal to one another. The powerplant reportedly achieved approximately two percent efficiency gains after three months of operations, resulting in an added value of $4.5M while reducing Co2 emissions equivalent to taking 66,000 cars off the road. Other potential capabilities of this technology include hyper-personalized customer experiences that can tailor energy plans and offer recommendations based on individual usage patterns, distributed intelligent grids with decentralized energy sources and autonomous management, and advanced asset optimization abilities to predict and prevent equipment failures through real-time sensor data and dynamic maintenance schedules.
The future will also continue to bring AI and ML ethical concerns and data privacy issues. It is vital that there is a balance of data-driven insights with consumer privacy and ethical considerations. While it’s natural for there to be fears of job displacement as automation continues to take on more roles, there are also opportunities for workforce retraining and adaptation plans to compensate. Of note, posts for AI-related job descriptions on the global work marketplace Upwork increased more than 1,000 percent during the second quarter of 2023 compared to 2022, a sign that humans remain a core component of future innovation.
About the Author:
Yuxin Yang is the practice manager of machine learning at TensorIoT where she builds cutting-edge solutions for clients with an emphasis on leveraging data science and machine learning. She holds a master’s degree in computer engineering from Stanford University and a bachelor’s degree in electrical and electronics engineering from Columbia University. TensorIoT is an AWS Advanced Tier Services Partner that enables digital transformation and greater sustainability for customers through IoT, AI/ML, data and analytics, and app modernization. For more information, visit tensoriot.com.
