Energy Efficiency and its Transformative Power
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For the last two weeks, the UN Climate Change conference COP21 dominated the news with discussions to fight climate change. The Kingdom of Saudi Arabia has also joined these discussions and submitted its Intended Nationally Determined Contribution (INDC) to the United Nations, seeking to achieve GHG emission reductions of up to 130 million tCO2eq by 2030 annually through economic diversification and adaptation. This presents a huge opportunity for the Kingdom given its scope for improvement in energy efficiency metrics.
On the 8th December 2015 the Middle East Economic Digest (MEED) intelligence inaugurated the MEED Kingdom of Saudi Arabia Mega Projects conference. The session was focused upon energy efficiency and sustainable buildings for the future. After a welcome introduction from Faisal Al Fadl, Chairman, Saudi Mega Conference and Secretary General of the Saudi Green Building Forum (KSA) and Co-Chairman Edmund O’Sullivan, MEED (UAE), DNV GL was given the honor of being the opening speaker of the session where Mohammed Atif, Area Manager for Middle East & Africa represented DNV GL. Mohammed Atif discussed the potential for energy efficiency savings in the Kingdom of Saudi Arabia and the region by focusing on optimizing the way in which energy is produced and consumed.
The energy landscape is facing a trilemma in optimizing the system to achieve three main objectives; reliability, affordability and sustainability. These three factors imply that an energy system has to provide continuous and reliable energy supplies, it has to be affordable and promote social equity and economic development and be a solution for current and future generations safeguarding the environment, thereby being sustainable.
Energy systems around the world are in the midst of a shift and transitioning from centrally controlled vertically integrated utilities to distributed intelligent systems with two-way communications, energy transfers, inter-temporal investment decision making and real-time operational decision making.
Energy intensity data from the UN shows the Kingdom is in a relatively worse position for industrial energy intensity and energy production of steel, when compared with Japan, UK, USA and China This is also shown by data from BP in 2014 where the Kingdom has the second highest energy intensity numbers with only Canada having a higher value when comparing 10 industrialized countries. Furthermore, Saudi Energy Efficiency Center (SEEC) data shows in 2013 that HVAC and Lighting performance on energy intensity was the worst for the Kingdom compared to four other countries.
In the year 2012, Chatham House a UK based think tank published a paper which showed that based on the current trajectories of production and consumption growth, the Kingdom would become a net oil importer before 2040. This projection made global headlines and whether the catalyst or not, there has been a flurry of activity on building institutions to manage the change to a more energy efficient environment in the Kingdom ever since. The same study also shows that the potential gain in reduced energy intensity through investment opportunities is significant in achieving the objective to save hydrocarbon resources and help reduce carbon emissions.
The Kingdom of Saudi Arabia and the rest of the region have a significant opportunity. As well as implementing regulations, policies, green building standards, energy service companies, building retrofit regulations and both supply side and demand side optimization, there is also an opportunity to technologically leap-frog by applying more advanced techniques especially as energy systems are decoupling and decentralizing due to disruptive technologies being applied on a mass scale.
One of those techniques where there is still much development work to do is machine learning. Machine learning is building computer programs that automatically improve with experience. In a decentralized system there are many more data flows, many more points for analysis such as forecasting the weather, forecasting consumption to a finer level of granularity, improved information on the condition and operational patterns of assets. A machine learning approach can help to analyze patterns, learn from past experience, draw on experience and evidence from other cases and improve the way in which optimization problems are solved. For example, a utility in Italy has saved around US$ 600M a year by apply machine learning to advanced smart grid and smart metering technologies.
By implementing a comprehensive energy efficiency strategy and development of regulations across industrial and residential sectors, the Kingdom of Saudi Arabia and other countries in the region could reduce the expected growth in energy consumption by 30% driven by a lower energy intensity index. These measures could include amongst others retrofitting of existing buildings and industrial processes, new green building standards, energy pricing reforms, system loss reductions, better forecasting of supply and demand patterns, distributed renewable systems and a smart grid combined with data analytics and machine learning techniques.