DATA COLLECTION MANAGEMENT
By embracing data-driven approaches, energy companies can better predict and manage supply and demand, mitigate
risks, and support the transition to a sustainable energy future. Duncan Bain, senior energy advisor at SAS, comments
T
ypically, when we think of the energy sector, we think about retail supply companies selling
power directly to consumers and businesses. If this were the case, managing the grid would - to all intents and purposes - be relatively straightforward. But there are many stakeholders involved, from network and system operators needing to access real-time information about supply and demand, to big enterprises generating their own electricity from renewable sources. This creates complex challenges that need careful management. Meanwhile, the new Energy Secretary, Ed
Milliband, recently published his list of immediate priorities and appointed Chris Stark, former head of the Climate Change Committee, to deliver 100% clean power by 2030. This is a huge request. The government has also lifted the restrictions on onshore wind, which means that there will be new investment and even more pressure on the transmission and distribution companies to connect new assets to the grid, with all the complexity that entails. In light of this, it’s impossible for energy
companies to overlook the key role that data and analytics play in accurate forecasting for supply and demand. Advanced cloud analytics can assist them in dealing with intricate problems and allow them to capitalise on new prospects. It enables vast amounts of data, gathered from smart meters, IoT devices, and weather forecasting systems, to be processed and interpreted.
THE ROLE OF DATA AND ANALYTICS The energy industry finds itself in the midst of a period of change, as it shifts from traditional load forecasting to multimodal forecasting models. This change is needed to take account of both new technologies and the changes in consumption we know are going to happen, but which there is no or little historical precedent for. For example, how will individual consumers
interact with schemes that reward them to actively reduce or increase their consumption to act as a distributed balancing mechanism? Or, how will rapid adoption of Solar PV create local constraints to grid infrastructure given its intermittent nature. This new approach, which leverages advances in deep learning and generative AI, is a big step forward from traditional methods which made predictions based on how much electricity would be needed at any given time, using historical data. For these more complex models to identify patterns and predict future energy demand and supply, data and analytics are critical. Real-time data integration further refines these predictions, allowing for adaptive forecasting that accounts for sudden changes in consumption or generation.
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THE CRUCIAL ROLE OF DATA AND ANALYTICS IN ENERGY SECTOR FORECASTING
The insights will also help the industry to shift
towards renewable energy. The industry itself must ensure that renewable energy is a practical and realistic option for people, to make participation possible for households across the country. Analysis of usage patterns and consumption levels
will help the industry move one step closer to achieving this, by revealing the requirements that people have of the network and enabling periods of high and low renewable generation to be predicted. This means that the sector can plan
accordingly. For instance, during periods of low wind or solar generation, alternative energy sources can be brought online to maintain balance. Conversely, during periods of high renewable generation, surplus energy can be stored or redistributed efficiently.
POTENTIAL CHALLENGES While data and analytics play an undeniably important role in forecasting, there are still some roadblocks to sourcing the data which is needed to make accurate predictions about supply and demand. This is a particular issue amid the transition towards renewable sources as it changes the state of play, but overcoming these challenges is essential to meet the government’s target of 100% clean power by 2030. The key to making accurate predictions is having
as much data as possible, and while the use of smart meter readings can help to understand when network reinforcement might need to take place, in the UK obtaining this data remains challenging. This is due to the supplier having responsibility over the meter, so a balance must be reached between ensuring the data can be accessed and used to improve the accuracy of forecasting, while also securing the privacy of the individual consumer. The uptake of solar panels
Duncan Bain
has further contributed to the challenges in this area. Solar panel output depends on weather conditions, making it variable and difficult to predict accurately. Furthermore, homeowners with solar panels sometimes feed surplus energy back into the grid, and this bidirectional flow of electricity requires precise coordination. These challenges can also apply to large corporations
with solar and wind deployments. Additionally, electric vehicles and heat pumps
introduce variable demand patterns that we don’t have a lot of historical data for, further complicating grid management. Demand flexibility schemes aim to balance supply and demand, but their integration requires advanced technologies and robust infrastructure to tackle the fluctuating supply and demand and ensure a reliable and resilient energy system.
MEETING NETWORK DEMAND Precise forecasting is crucial for maintaining a consistent, efficient, and reliable energy supply, ensuring the network meets real-time demands effectively. Overestimation of supply can result in insufficient energy production, causing shortages and potential blackouts. On the other hand, underestimation can lead to overproduction, wasting resources and increasing operational costs. These imbalances can stress the grid, reduce reliability, and elevate costs for both providers and consumers. Inaccurate forecasting also leads to financial
instability. Energy companies may face fines and penalties from regulatory bodies for failing to ensure a reliable power supply. Market volatility can increase, leading to higher energy prices and economic uncertainty. This instability deters investment in the energy sector, slowing down the development of infrastructure and technologies essential for the energy transition. As the world moves towards renewable energy, the role of data and analytics will only become more critical. As the energy landscape continues to evolve, the importance of leveraging advanced AI-powered analytics to navigate these changes cannot be overstated, with many companies already doing so. For example, Repsol relies on SAS Energy Forecasting to optimise its energy usage, while Enel Green Power is using SAS’ IoT-powered solution to monitor their fleet of wind turbines - reducing the time to complete Wind Power Generation Unit analysis from one month to two days.
SAS
www.sas.com/en_gb/software/energy-
forecasting.html
ENERGY & SUSTAINABILITY SOLUTIONS - Autumn 2024 35
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