Machine Learning & AI
How Growth-Stage Energy Companies Can integrate AI
Companies in the introduction stage of the product life cycle (PLC) are focused on the development and various costs sustained during production. At the growth stage, the company has seen product sales, increased revenue, and a bright outlook on forecasted earnings. These growth-stage energy companies can integrate artificial intelligence (AI) to provide dynamic management necessary to optimize power grids, increase energy efficiency, and reduce their carbon footprint.
According to Indigo Advisory Group, 80 percent of data in a company is usually unstructured. The implementation of AI will assist with energy conversion and maximize the utility operational systems by optimizing data mining, analysis, and restructuring.
Smart grids are optimized power grids that utilize digitized communication to identify and respond to localized variations in energy usage. AI can be integrated into the main foundational stages of energy companies’ operational management systems.
- Renewables Management System (RMS): The RMS provides a centralized data hub for optimized management of renewable resources like wind, solar, and hydro power. Predictive modeling can be used for forecasting renewable resources and augmenting the operational functioning of all plant equipment. High-caliber sensors are used to gauge radiant energy from sunlight and wind speed on turbines. Diagnostic algorithms can evaluate energy storage capacity and lifespan for energy storage units.
- Demand Management: AI, self-learning, and predictive modeling can make energy generation and distribution more efficient. Facility energy consumption data is tracked so that accurate performance levels can be assessed.
- Infrastructure Management (IM): Machine learning algorithms are useful in data mining, emphasizing probable issues, and executing decision-based action steps.
Akeptus lowers energy costs by analyzing facility energy use, data mining company records, evaluating cause and effect, suggesting improvements, and executing action steps. This living Application Programming Interface (API) uses image recognition, natural language processing to identify comments on work order requests, and predictive modeling to forecast energy usage. It is decision-based, and utilizes complex algorithms, machine learning, and deep learning. Users benefit from customizable parameters that can operate on autopilot and quickly identify impending problems before they occur. By managing floor plans, overseeing plug load devices, and regulating thermostats, users can become more energy efficient. Energy data, occupancy assessment, and temperature data is captured and mined with pattern recognition techniques. Akeptus uses natural language processing (NLP) to extract data and interconnect concealed perceptions so data is mined and analyzed, and the software automatically creates an understandable report.
Integrating AI into your company’s intelligent energy management system (IEMS) will enhance your facility’s capacity to operate smoothly, and become more energy efficient.