IoT Agenda

Oct 16 2019   2:29PM GMT

How IoT is transforming energy efficiency tactics

Mike Jeffs Profile: Mike Jeffs

Tags:
Automation
commercial IoT
Internet of Things
iot
IoT analytics
IoT benefits
IoT power
IoT sensors
Machine learning
retail IoT

The level of electricity generated in the UK last year was at its lowest level since 1994, with only 335TWh of electricity produced, according to Carbon Brief.

Although the figure was only a small reduction since 2017, it was substantially lower than 2007, which was the peak of electricity production in the UK.

As the country becomes more conscious of its energy consumption, there is a growing demand to reduce our carbon footprint. Businesses are aiming to become more efficient with their energy usage and using renewable sources where possible. Output from renewable sources in 2018 rose to a record high, contributing to 33% of the UK’s total energy consumption. There has been a 95TWh increase in renewable output since 2005.

With major shifts towards energy efficiency, IoT will become integral to help many businesses provide critical information on energy monitoring.

Source: Hark

Driving energy efficiency

The retail sector is just one of the many industries that’s seen a rise in overhead costs because of energy bills. IoT applications will enable retailers to improve energy efficiency with real-time tracking and monitoring insight.

A combination of sensors from existing systems and additional IoT sensors can be used to create a unified feed of data. The additional IoT sensors can be integrated into key assets in the stores, such as HVAC systems and refrigerators. Data is collected from each of these sensors to create an interconnected network of devices that feed data into the cloud. This unified feed is used to influence decision making and add intelligence in real-time.

Due to the real-time nature of IoT technology and the data it provides, business are able to optimize their operations, and prevent asset failure and subsequent loss of energy.

Source: Hark

Predictive maintenance with machine learning

Machine learning algorithms use collected information to highlight potential failures and inefficiencies within a company’s operations. Slight changes in energy patterns at a micro and macroscopic level can indicate possible control problems or failures in components such as compressors and heating elements. The algorithms can automatically analyse patterns and monitoring assets, creating real-time alerts to potential areas of concern to prioritize callouts.

This can prevent downtime, reduce callout charges and mitigate loss of product. For example, sensors integrated with refrigeration systems can provide insight into how it’s operating through power draw analysis, and ensure that any issues are fixed before failure occurs, preventing produce spoilage.

Automation helps save on costs

IoT is allowing for automation within retail and other industries through the speed and accuracy of real-time information. A great example of this is lighting. Lighting is a high cost for all retailers; but if lighting grids could automatically react to external, ambient light levels, there’s a huge potential for conserving power.

This concept is especially useful during triad periods when energy costs are at their highest. All stores should aim to reduce energy usage during peak times and if they are equipped to do so, move to backup generators to avoid the high charges altogether.

The Power Factor for energy efficiency

A key performance indicator for energy efficiency is power factor. In technical terms, the power factor of an AC electrical power system is the ratio of actual power to apparent power. A lower power factor results in more electricity being drawn to supply the actual power.

Retailers should aim to improve their energy efficiency by increasing their power factor and reap the benefits of reduced energy costs. Power factor ranges between negative one and one, with one being totally efficient with no energy wastage. However, it is technically impossible to reach one as there will always be some form of heat loss, so a power factor between 0.95 to 0.98 is an acceptable range. It is important for a retailer to monitor this number and aim to be as close to one as physically possible.

HVAC systems and lighting systems are main contributors to a bad power factor. A smart solution ensures that sensor data is analysed in real-time to evaluate performance, allowing businesses to identify the exact piece of equipment that is lowering the power factor.

Key metrics analysed can range from power factor fluctuations, kilowatts draw, individual phase frequency, amperage and volts.

By having control over their energy systems and monitoring their power quality indicators, a retailer can benefit from range a of advantages such as significant and immediate savings on energy costs, longer device lifetime, and reduction in low-power factor penalties and carbon footprint.

With such significant benefits that can be gained from implementing IoT solutions, it’s no wonder there has been huge growth in the sector.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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