Maurizio Canton, CTO EMEA at TIBCO Software, takes a look at the role data analytics can play in increasing energy efficiency and sustainable development.
It’s little surprise that the term ‘throwaway society’ has become such a regular soundbite. From the fruit that gets discarded if it doesn’t conform to the perfect aesthetic, to fast fashion produced at the expense of rivers polluted by chemical overspills from garment factories, the evidence is all around us. Then there is our ever-rising energy consumption. Fuelled by an expanding population and growing reliance on electricity, the US has recorded its fastest rate of consumption in 30 years, according to figures from British Petroleum. It also confirmed a 2% hike in carbon emissions -the fastest growth for seven years.
Energy management and the challenge to save costs, optimise the lifespan of assets and meet the stringent compliance on which they are increasingly judged, has become an all-consuming battle. We are now witnessing a growing roll call of directives aligned to some ambitious carbon-cutting targets, not least the Energy Savings Opportunity Scheme (ESOS) Phase 2. With its fast-approaching December 2019 deadline, this UK government initiative is putting the energy consumption of larger companies under scrutiny, promising a hefty fine for those who fail to comply.
Against this backdrop, it is perhaps no surprise that a more scientific and forensic approach to energy optimisation needs to be deployed by businesses. Reliable and consistent performance data, built to analyse consumption patterns and identify the factors that affect it – all in real time – becomes the lynchpin of smart energy management. Far from a ‘nice to have’ extra, bringing Big Data to this arena, along with Machine Learning and Artificial Intelligence, will be an intrinsic ingredient to stay competitive, cost effective and achieve sustainable growth.
This marriage of energy and technology, however, still remains an under-exploited dynamic. It is a source of untapped potential for better assisting both customers and utility providers to work together. Unlocking this synergy is critical if we are to meaningfully address the growing supply and demand issue of dealing with finite resources.
Indeed, as one of the few controllable costs in most organisations, the precision that data analytics brings to energy management is a natural fit in the arena. Specifically, the ability to predict asset life, building usage and responding accordingly to many of the fluctuations that can have a significant bearing on performance. For example, the output from wind power and solar power, the two most significant renewable energy power generation methods in the SMART grid, is significantly affected by weather conditions. The efficiency of these power generation methods is positively impacted when adding both accurate forecasting from analytics and integration with GIS data (Geographic Information System) into the mix.
Only by unearthing the information and insights from SMART meters, costs, production figures, assets and business policies and, most crucially, drawing all this intelligence together in one place, can you overcome one of the most long-standing barriers to effective energy management.
Traditionally, the often-fragmented nature of an organisation’s infrastructure, siloed operations and reliance on legacy building management systems, has been prohibitive to achieving a clear picture of energy usage and opportunities for improvement. It’s why, even in today’s factories, it isn’t unheard of for gas-powered air cylinder systems to have ageing pipes that leak so much air that it makes an audible sound around the site, haemorrhaging profits as well as resources.
It may seem inexcusable waste, but it can be attributed to one of the most prevailing oversights, common across so many businesses. Too many businesses continue to overlook the extent of the integration required to effectively bring together people, systems, data sources and solutions.
How ever disparate the locations or assets, a system that can connect and integrate data sources at speed – through a single point of access – and enable users to interact dynamically with the analytics that puts data in context, is essential. This foundation of fast, proactive responses and decision-making will ultimately benefit both the planet and profits.
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