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Smart Energy Management and Strategic Decision Making Platform

Periodic Reporting for period 1 - W-THINK (Smart Energy Management and Strategic Decision Making Platform)

Reporting period: 2017-02-01 to 2017-07-31

The way industries manage and consume energy is unsustainable. Currently, the industrial sector accounts for one-third of world energy use and industrial energy consumption is set to rise by 50% in just 20 years. This will dramatically increase energy costs affecting companies’ margins and productivity. On average, industry spends one-third of its operating budget on energy. €110billion and up to 35% of total industrial energy could be saved every year, with the application of energy-saving and low carbon practices and technologies.
Heating, Ventilation and Air Conditioning (HVAC) is responsible for around 40% of the energy consumption in industrial plants. Frequently, this is the largest energy consuming type of equipment and it therefore provides significant scope for saving energy and money.
While a proportion of the potential savings would be realised by the substitution of old and inefficient plant, an important share of those can be achieved by optimising the operation of existing plant, from energy generation systems to distribution and final delivery.
At Wattabit we have piloted W-THINK, an Intelligent Industrial Energy Management System (IEMS) that autonomously optimizes and reduces energy consumption used to meet HVAC and superheated water demands using state of the art Industrial Internet of Things (IIoT), Artificial Intelligence and machine learning algorithms. Other relevant characteristics of the software solution include the following:
• Hardware neutral, implemented as a software layer on top of the existing SCADA
• Cross-sectorial as it acts on horizontal loads (heat, coolth, HVAC and super-heated water)
• Constant learning and readjustment of algorithms for enhanced operation and related savings
• High energy savings with low investment
• High impact on primary energy consumption reduction and therefore cost
A result of a EU 7th Framework Programme funded research project, the system is currently installed in the major car manufacturing plant in Spain, with a yearly expenditure of over 400M€. The iIEMS manages 10% of the total loads in the plant, achieving savings of approximate 10% in cost. The system is integrated with the existing SCADA systems and provides feedback to the plant managers by means of dedicated screens in the plant’s control room (see Figure 1: W-THINK in operation in the control room).
The current product is categorized as a TRL 6, and through the current project Wattabit aim to bring the current technology to TRL 9. The specific objectives are as follows:
• To increase the analytical power of the system. Machine Learning capacity and prediction precision of W-THINK algorithms will be further improved (See section 3.1);
• To develop W-THINK as an off the shelf SaaS solution that can be easily and cost effectively installed and integrated with existing monitoring equipment in industrial sites. The solution will be developed according to the results obtained in the feasibility analysis of phase 1;
• Test W-THINK in at least 5 different industrial plants, including the automotive sector (agreement with SEAT), metallurgic/cement and data centres;
• Final validation of W-THINK by future potential customers. We also aim to validate the results achieved in 2015 in other industrial sites operating in different sectors
Phase 1 of the SME Instrument has allowed WATTABIT to perform a market study to relevant potential users of the technology to be developed. Such market study has informed technology and business model insights. Most of the technical gaps were already identified in a previous pilot project, and hence have been confirmed in this market study.
The main result of the present submission is a busniess plan for the explotation of the product to be brought to the market, with a promising outlook five years beyond project completion, and an expected exponential growth. Barriers to market penetration have been identified, and work is starting to mitigate their impact.
Phase 2 of the project will allow the development of a commercial product that will be able to achieve important energy savings and CO2 emissions reductions in the industrial sector through an artificial intelligence optimisation tool. Should the business plan be met, it is clear the technology has a high commercial potential. Projections include strong presence in the European, American, Latin American and Asian Markets within five years of project completion.