Unravelling the business value of a corporate non-technical losses strategy for utilities was the heading of a webinar hosted by MSEI and Choice Technology earllier this year. The webinar focused on how the deployment of sophisticated algorithms and advanced analytics technologies can reduce utilities’ non-technical energy losses.
Benefits of analytics
Rui Mano, Global VP of strategic alliance and co-innovation for Choice, said these solutions are helping energy providers to recover $40 billion in lost revenue per annum. It is, however, important to note that revenue recovered is high depending on the number of utility customers and energy losses.
In Brazil, Choice Technology has helped a utility with 4.5 million consumers to produce a 100% increase in recovered energy within a period of 24 months. 150GWh/year was added to the company’s revenue.
The solutions provider claims its analytics technology helps utility companies:
- Maximise revenues with the same operational budget
- Gather and analyse data from customer behaviour and operational data utilising existing utility databases and applications, applying artificial intelligence and machine learning algorithms to identify fraud patterns
- Prioritise inspections based on risk rating and financial impact
- Identify inconsistent results that indicate errors in processes and systems, such as billing processes and others
- Identify unexpected outcomes from internal fraud
- Calculate and monitor KPIs related to NTL operation
- Accurately predict full profitability and associated financial returns for individual customers.
- Reduce disconnections and accidents due to illegal, unsafe grid manipulations.
- Lower tariffs, reduce carbon emissions and increase the competitiveness of a utility compared to other utilities providing the same services.
- Reduce energy consumption, improve energy efficiency and help energy providers delay investments in new energy generation, transmission and distribution infrastructure. You can access the webinar here...
Global use of analytics
Developing countries such as Mozambique and Jamaica which previously used prepaid metering systems to address non-technical revenue losses are adopting analytics as the most cost-effective solution.
A recent study conducted by Northeast Group found that smart metering solutions cost an average of $215 per meter while analytic technologies cost an average of $2,25 per meter per year.
On the other hand, according to the World Bank, electric utilities lose almost $100 billion worth of energy due to theft and fraud. Over the past decade, utilities’ non-revenue has increased by almost 15%. For instance, in Angola, 47%/$117 billion worth of energy generated is lost to fraud and theft per year. In Germany, the percentage of energy losses is at 0.9% equivalent to $1,5 billion per annum.
“One way or the other, non-revenue expenses will be transferred to consumers, if not in tariffs, through taxes,” cautions Mano.
Such factors are driving an increase in the value of the energy analytics market on a global scale.
Research and Markets forecasts the utility and energy analytics market to grow by 13.04% by 2020 from 2016 level. Revenue generation is expected to grow from $1.62 billion to $2.99 billion.
Analytics technologies in smart cities
Government policies on energy efficiency and carbon emission reduction will play a significant role in improving the size of the market. A report conducted by PWC highlights the possibility of 2.7 million jobs to be created in the data science and analytics market by 2020. Mainly driven by efforts by the government and organisations to use analytics to innovate, the US data science and analytics market is expected to employ an additional 190,000 workers by 2018.
Cities and municipalities are also using analytics to save money and time in their business processes. According to Harvard University, leading cities have improved the accuracy of services through the use of analytics. For instance, it is estimated that City of Boston is saving approximately $1 million a year in energy costs.
In Massachusetts, the suburban town of Wellesley has reduced energy usage in buildings by 9% in three years by including the use of a data analytics software in energy management programmes in which LED bulbs and other energy efficiency appliances were deployed.
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