Energy & Utilities
Use Cases
Newsfor Energy & Utilities
Automate building systems to reduce energy costs through AI powered adaptive temperature and energy control
Automate building systems to reduce energy costs through AI-powered adaptive temperature and energy control. The challenge is to justify the investment costs against actual energy savings.
Automate control decisions in control room environments to reduce cost and human error
Replicate (and replace) human-made decisions in control room environments to reduce cost and human error
Automate image labelling of components to support complicated infrastructure management
In environments of high complexity and potential risk being able to label components automatically can help with mutliple operational tasks.
Detect potentially dangerous electrical current surges
Detect potentially dangerous electrical current surges. This potentially enables systems and devices to automatically react in an appropriate fashion to minimise risk to users and devices.
Digitise analogue meter reading through computer vision
Updating and replacing legacy analogue meters with digital meters can be an expensive and complicated process. If instead IoT cameras are positioned to capture images of the read-outs and translate those to digital read-outs then the information can be captured automatically. This enables greater speed, frequency and consistency of data capture, along with reduced risk and cost from human checking - potentially enabling multiple other use cases.
Examine ground composition to reduce required volume of exploratory drilling for samples
Interpolate data on ground composition to reduce the required exploratory drilling samples. This increases speed to market and reduces the potential workload.
Identify potential fraud from utility consumers
Monitoring and indentifying potential fraud and theft from utility networks
Optimise blend and process timing for raw material inputs to refining and similar processes
Optimise blend mixture and process timing of raw materials being used in refining and similar processes. Sustained small marginal improvements can have a significant cost impact - especially where human involvement can be costly or potentially hazardous.
Optimise energy scheduling and management of power plants based on energy pricing, weather and other real time data
Optimise energy scheduling and management of power plants based on energy pricing, weather and other real-time data
Optimise extraction plans based on data including drilling samples and performance at historic and comparable sites
Optimise mine extraction plans based on drilling samples, past sites, and other data including satellite and drone footage
Optimise performance and positioning of wind turbines
With sufficient warning wind turbines can be positioned at optimal angles to extract maximum benefit from fast-changing gusts of wind. in concentrated fields of windmills the windflow interaction between turbines can also add to the complexity that needs modelling.
Optimise pricing and promotional targeting to energy customers
Optimise pricing and promotional targeting to energy customers. Factors will include issues such as weather projections, spot supply pricing and forward cost estimations.
Optimise treatment of processed materials to enable reuse and to reduce environmental impact
Optimise treatment of processed materials to enable reuse, saving cost, and to reduce environmental impact, also potentially reducing legal and negative PR risk.
Predict commodity requirements
Predict commodity requirements - typically to optimise procurement strategy for large industrial organisations.
Predict energy demand trends based on data sources ranging from sensors to social media
Predict energy demand trends based on data sources ranging from sensors to social media. In a competitive market the more varied and unique the data sources accessed the more refined (if not always more accurate) the model can become.
Predict optimal location and specification in construction for power generation equipment
Optimise location and building specifications in construction of power generation equipment based on previous sites and other relevant data including visual imagery (e.g. from satellites or drones). Demand projection scenarios need to be fed in to the predictive model.
Predict problems and recommend proactive maintenance for fixed equipment such as substations or electricity pylons
Predict failure and recommend proactive maintenance for fixed (substations, poles) and moving equipment
Predict problems and recommend proactive maintenance for mining, drilling and support equipment
Predict failure and recommend proactive maintenance for mining, drilling, power generation, and moving equipment
Predict problems and recommend proactive maintenance for power generation and supporting equipment
Predict failure and recommend proactive maintenance for mining, drilling, power generation, and moving equipment
Smart home devices manage consumer energy expenditure
Smart home devices manage consumer energy expenditure by, for example, turning lighting, heating on and off depending on human presence, pre-planned variable margins (e.g. average home temperature) and weather / time of day.