Harnessing AI intelligence to maximise energy procurement
Global companies should be sitting on the forefront of sustainability and renewable energy procurement. Encouragingly, research from PwC revealed that the majority of UK business leaders intend to increase their long-term investments in sustainability initiatives as they grow concerned about the impact of climate change. In addition, nearly 50% of Fortune 500 companies have already set sustainability and renewable energy targets.
For procurement leaders, their goal for 2021 is clear: find the best value for their company while continuing to transition to high-quality renewable energy sources. Fortunately, the evolution of new technologies is making the process simpler and more transparent than ever before. Procurement leaders can harness an artificial intelligence (AI) to identify opportunities in the quickly evolving renewable energy market, while safeguarding their businesses’ current renewable portfolio. So, how can procurement leaders implement AI intelligence within their operations to improve business outcomes?
Determine energy suitability in real-time
The tricky part for many procurement teams is staying on top of the continuously evolving renewables market, which is made up of disparate systems and a diverse range of providers. To identify the most suitable energy options for your business, the key is to integrate both historical data, such as existing purchasing workflows, with real-time data from the local and global renewables market. For example, AI algorithms can combine existing data sets, through simple API-based data integrations, with external data sets to provide a single view of the renewable energy ecosystem. Then, as new opportunities arise within the market, your AI technologies will cross reference with historical data to quantify suitability, particularly in regard to your company’s specific budgets and timelines – saving significant time spent on manual research, RFPs, and handling costly errors.
Harness predictive intelligence for data-driven decision making
After a year of budgets being scrutinised and leaders being tasked to do more with less, building a clear view of all potential outcomes is vital for any renewable energy investment. When identifying the most cost-effective suppliers, AI algorithms will utilise historical data relating to electricity demand, energy generation, and fuel prices to predict price forecasts across markets. Machine-learning algorithms also correlate sensor and climatic data to predict energy needs according to seasonal variations, as well as align energy demand with grid load and outages. In addition, sophisticated AI models will analyse supply and demand by simulating price dynamics and economic events, providing the commercial implications of various scenarios. Altogether these capabilities enable procurement teams to clearly see the existing and potential results, enabling informed and risk-adverse decision making.
Minimise risk in energy purchasing
Traditionally, PPAs encourage buyers to use only one supplier – meaning organisations tend to rely on low risk, high volume providers. But this approach often takes months of manual research and RFPs. However, by implementing AI technologies, procurement teams can standardise the energy acquisition process to maintain a more dynamic and diverse energy portfolio. AI algorithms, for example, can produce the perfect provision of green energy from low-risk high-volume suppliers, while standardising partial fulfilment agreements with independent initiatives and new developments, furthering the positive impact of public sector green energy investment across the growing renewable grid. In turn, by grouping PPAs and automating the complex legal workflow, procurement teams will save a magnitude of time, money and personnel
Demystify the outcome of renewables investments
To ensure there is no obscurity around the actual output of each energy investment, procurement teams should harness AI models to manage and measure their investment portfolio. Real-time intelligence can be delivered both quantitatively, with reportable checklist-style scoring, and qualitatively, with a more in-depth commentary on progress. Outcomes can then be cross-referenced across inter-connected targets to deliver short-term and long-term insight, and to report to the relevant parties, enabling a more transparent and accountable approach to emissions reporting.
The business case for adopting renewable energy is becoming clearer on a daily basis, with the climate emergency more urgent than ever. As such, pressure will not ease on procurement to lead their organisation to a more sustainable future that is underpinned by renewable energy. While this may seem like a ‘nice to have’ for many businesses, it is certainly achievable. A recent example includes the tech giant, Facebook, successfully reaching its target to power its global operations entirely on renewable energy. Now, the company can focus on reaching net-zero emissions across its entire value chain by 2030.
Looking ahead, it is important that organisations pursue their own unique sustainability journey – and a lot of that responsibility and accountability will lie with procurement teams.
With intelligent capabilities underpinned by AI technologies , renewable energy procurement can be simpler and more transparent than ever before. In 2021, procurement leaders should harness AI capabilities to gain a single view of the quickly evolving renewable energy market, aligning this with their own business operations. Only then will they be able to make data-driven decisions while effectively managing risks and costs. This level of insight will also be vital for procurement leaders to prove that they are truly driving impactful results for their company.
Muhammad Malik is CEO and Founder of NeuerEnergy