energy blog

Cognitive and predictive tools for energy

AI-driven software and/or consultancy platforms which cut out overly-complicated non-standard processing, subjective or biased decision making and human error are only now just starting to appear in the energy sector

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Energy can be a new tech sector

Perhaps because of its relatively recent privatization, the energy sector has always had a tendency to fall back on established industry best practice. One of the products of a “BaU first, innovation last” culture has been a slow uptake, or worse, dismissive attitude to building and deploying cognitive and predictive services and tools, despite an awareness of the limitations in using the increasingly archaic systems and processes in operation today. There may also be a perceived complexity in delivering these tools into the sector.

Some competition for AI software-driven services and tech solutions in the energy industry exists, with notable “disruptive” leaders include Electron, Bulb, Origami and energimine offering distinct customer services by taking slick process automation to a level not previously seen or seeking to extract business value from applying machine learning, natural language processing and blockchain to energy. The ongoing collaboration between NGT (the UK grid operator) and Google’s Deep Mind to facilitate balancing energy supply and demand is also receiving increasing media attention and an exciting prospect. However in general across the industry there remains an absence of AI-driven software and/or consultancy platforms which cut out overly-complicated non-standard processing, subjective or biased decision making and human error.

These gaps are particularly in evidence across the operational, procurement, trading and analysis functions carried out by various energy players. They are also in evidence in onboarding new hires into the sector, and in the energy advisory/consultancy services market.

Embedded value in providing an enhanced energy-AI service would not only reduce obvious operational overheads but should drive future revenue generating avenues when delivered into the entire B2C (domestic though SME and I&C) customer and B2B (energy suppliers and utilities) client energy consumer landscape.

The market opportunity is immense: Across all sectors McKinsey have digital companies (Google, Baidu etc.) investing $20-30 billion in AI in 2016 with venture capital firms and private equity firms investing $6-9b. PwC project $15.7tr as the potential AI contribution to the global economy by 2030 while AI services into the industrial sector are expected to grow at a compound annual growth rate (CAGR) of 53% in the next five years. It’s this opportunity that ChangingEnergy is targeting by applying IBM Watson & Bluemix APIs and services to build an education, analysis, forecasting and trading support tool for the energy sector.

To find out more contact ChangingEnergy or test for yourself our BETA-version virtual agent/ChatBot-driven machine learning (ML) and natural language understanding (NLU) application.

Test drive an AI-energy app
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