How To Empower Smart Energy Management With Predictive Analytics

Dec 12, 2016 posted by Dr. Atul Sharma under SMAC, Business Intelligence

How to Empower Smart EMS with Analytics

The rapid increase in energy costs is forcing enterprises to look for smarter ways to manage energy. Energy suppliers have levied huge penalties on enterprises that are using inefficient assets or devices having low power factor. In addition, government is raising the bar for complying with energy standards and reducing carbon footprints.

Effective and smart energy management systems (EMS), integrated with predictive analytics and Internet of Things (IoT), is an ideal solution to address these challenges along with radical changes in monitoring and managing energy consumption.

Effective Energy Management

The need for energy management is becoming a critical focus area for modern energy service providers and end-customers. Consumption of energy should be minimized without compromising on comfort and other ergonomic considerations like humidity levels, fresh air, level of carbon dioxide, etc.

A smart EMS helps optimize energy consumption for heating, air conditioning, ventilation, , lighting, refrigeration, security systems, and fire systems, thus ensuring energy is used as and when required.

An advanced EMS ensures monitoring of building conditions, utility sub-metering, equipment status, climatic data, as well as demand limiting that involves load scheduling, duty cycling. Such a system focuses on maintenance (remote operation and equipment control, generate maintenance schedules, and breakdown diagnosis), and record generation (energy conservation documentation, and modification/replacement analysis).

How Real-time Analytics Helps In EMS

Smart grid initiatives improve operational efficiency, maintenance and planning by ensuring each component of the grid can listen and talk. A major component of this technology is the automation process.
Real-time analytics helps a smart EMS by optimizing energy production, building-energy management, weather forecasting, predictive maintenance for equipment, demand response etc.

Leveraging IoT and predictive analytics helps enterprises realize the following energy management benefits:

  • Asset efficiency analysis: Real-time collection and analysis of varied data helps in calculating asset efficiency. Moreover, it helps determine cost implications of non-optimal performance of devices.
    Asset efficiency can decline due to maintenance issues or age factor. Techniques like benchmarking help in computing the optimal efficiency levels of devices under specific environmental conditions. You can use this data to identify situations wherein excessive energy is being consumed in your building.
    You can also calculate the cost implication of maintaining energy devices with the help of predictive analytics.
  • Root cause analysis: It is us when the energy consumption level is higher than the expected levels. IoT enabled data is used to validate the hypothesis generated from root cause analysis.
  • Warranty Lifetime Analysis: Predictive analytics helps in determining the optimal warranty period and cost of your equipment/device. If you observe that some devices fail at the early stages of the lifecycle, you can avail a longer warranty period to cover these failures. The device may not fail as a whole but some components may be prone to failure. In addition, warranty analysis becomes prominent in the case of error-prone components of an expensive device.
  • Carbon emission analytics: This technique helps in minimizing overall carbon emissions from commercial buildings. You can implement energy management standards governed by specific laws for the simulation process. You can customize energy management solutions for a specific area.
  • Correction/exception analysis: Predictive analytics help identify areas requiring power correction to reduce energy costs. You can analyze where power factor correction might be required early on to enable cost savings.
    Accurate analysis of alarms, exceptions or manual overrides brings forward the discrepancies registered during operations at regular intervals. With the help of big data analytics you can determine the optimal state, possible reasons of deviation, and preventive action for running automated operations.

Harnessing The Power of IoT and Big Data

Industry experts Gartner predict that by 2020, the world will have 20.8 billion connected devices, with 5.5 million daily new connections in 2016. IoT has made data collection easier; devices record even the smallest amount of data that is generated and transfers that data across the entire network seamlessly.

Algorithms are based on pre-defined parameters, which are deployed for monitoring and controlling the use of energy. In addition, predictive analytics helps enterprises forecast the power usage, which provides flexibility to avail the services of utility providers at lower rates.

You can provide integrated energy usage system across different locations and functions with the help of predictive analytics and IoT. Now, enterprises can visualize energy consumption by utilizing sensor data. You can use cloud-based analytics to generate actionable insights with the help of dashboards and data analytics engines.

You can use predictive analytics for enabling smart insights for intelligent decision-making. For example, Qualcomm uses BI and sensor data to detect the levels of building occupancy levels; the results are used to control real-time HVAC and lighting systems to reduce energy costs.

Enterprises can use predictive analytics and BI to predict asset performance and future energy usage pattern. Relevant insights help to establish an EMS to optimize energy use by switching off some equipment during peak hours or running the efficient equipment periodically.

A high level of automation helps energy managers forecast their energy needs, plan asset maintenance, which enhances the operational efficiency. A smart energy management system reduces the overall carbon footprint of your enterprise. You can expect to reduce your energy costs by minimum 20%.

Top Technologies to watch

  1. Digital power conversion: By late 1880s, large-scale high-voltage transformers had created a stable platform for electrical grids, which is being used even now. Normally, a transformer is 250 cubic feet long, weighs 10,000 pounds, and costs up to $20,000.
    Digital switches, developed for power management in high-frequency devices ranging from military jets to rails are made of gallium nitride and silicon carbide. The high-speed digital switches use 90% less energy, and occupy only 1% of the space as compared to the traditional transformers.
    With the growing popularity of emerging technologies and applications, traditional transformers will be replaced by 2020 at one-tenth of the total cost.
    Presently, China is well positioned to take advantage of this opportunity due to its planned grid expansion.
  2. Grid-scale storage: Innovations using liquid-metal batteries, flow batteries, flywheels, and ultracapacitors could help you reduce costs to $150 to $200 per kWh by 2020 thereby creating opportunities of providing grid storage in every major metropolitan market.
  3. Electrochromic windows and ACs without compressor: Currently, running a high-efficiency air conditioner ranges from $3000 to $4000 per year for a hot region. The windows account for 50% of cooling energy to escape. Digital technologies like compressorless air conditioners and electrochromic windows reduce home-cooling costs by 50%.
    Electrochromic windows change the shading based on difference in the external and internal temperature. The compressorless air conditioners come with dessicants to dehumidify the air. Advanced windows keep the cold out and provide great insulation in cold climates.
  4. Eco-friendly coal: Technology innovations enable coal-fired generators capture above 90%carbon dioxide in less than $2000 per kilowatt. Currently, it is predicted that coal with carbon sequestration would be cheaper, reliable, and widely deployable than other renewable technologies
  5. Electrofuels/Biofuels: The exorbitant price of crude oil is rapidly increasing the need of adopting biofuels like corn ethanol. Innovations focus on algae-based and cellulosic biofuels for producing high-margin specialty chemicals that is generating huge revenue now and setting a trend for the future.  

Challenges

The entire ecosystem of energy management is witnessing rapid changes. Hence energy companies must:

  • Maintain reliability and stability of power grid
  • Improve energy efficiency across entire value chain
  • Merge intermittent CO2 free energy

The rapid increase in energy costs, mass electrification, and climate change will determine the pace of digital transformation of energy management systems. The smart grid technology is a fundamental re-engineering of such systems.  

Regardless of the pace at which you adopt smart grid technology and systems, it will definitely be a gamechanger and transform your business processes and technologies.

The Way Forward: Digitizing energy management

Implementing IoT and predictive analytics will optimize energy consumption thereby resulting in well-informed and intelligent demand-side operations. Digital disruption enables collaboration between utility service providers and enterprises.

In the rapidly evolving energy value ecosystem, utility services are provided through demand-response systems, wherein just-in-time (JIT) methodology is used to supply power. Though the JIT method is not new, IoT is helping enterprises to reduce service provider costs with the help of real-time monitoring and predictive analytics.

Algorithms are used to predict the availability and reliability of alternative sources of renewable energy to drive the JIT energy.

Digitizing energy management systems is a golden opportunity for enterprises for improving their operational efficiency along with achieving the eco-sustainability targets responsibly. Converging digital technologies with energy management systems will definitely pave a new path to create a new services’ ecosystem involving smart cities.