Predictive analytics and Machine Learning crucial to energy

Predictive analytics and Machine Learning are crucial to energy decarbonisation, according to a new report from data specialists CKDelta

Predictive analytics and Machine Learning (ML) have a critical role to play in energy decarbonisation, according to a new report from data specialists CKDelta.

Pioneering cross-sector change and collaboration calls for greater collaboration in the utilities sector, to fulfil its climate ambitions.

Managing Director Geoff McGrath said the potential to integrate this data across the value chain means we can re-conceptualise how we think about, and deploy, systems with both embedded and adaptive intelligence to optimise system performance, without compromising the net zero goal.

“The utilities sector is at a watershed moment," he said. "The eyes of consumers and regulators are firmly fixed on electricity, water, and gas providers across the UK. Cost, environmental impacts, and consumer satisfaction are changing the way the sector delivers for customers."

Combining insight from water services provider Northumbrian Water Group, the report examines how the utilities sector can address four key challenges facing the industry today – including leak reduction, shifting patterns of usage, and the emergence of a new energy economy – by deploying open-source, data-driven models.

It comes at a time when electricity, gas, and water companies are coming under increasing regulatory and consumer scrutiny and the sector is driving forwards with ambitious environmental targets. The water sector has committed to delivering net zero emissions by 2030, while the government has committed to decarbonising the electricity grid by 2035.

Highlighting shifting patterns of energy and water usage as a core challenge to achieving these targets, the report states that we need integrated solutions that can accurately accommodate and predict both emerging and static trends.

Machine Learning data models can optimise energy systems and networks

It identifies predictive data models developed from Machine Learning with high-frequency data as one such solution, noting that these models could also play a key role in optimising existing systems and networks.

The report goes on to suggest that companies and their investors should rethink their approach to effectively address the challenges posed by delivering a low carbon future, adopting whole systems models to gain visibility of competing aims across networks. These models empower organisations to holistically assess alternative energy and investment needs against other commercial targets, such as cost reduction.

CKDelta conclude their report with four recommendations, which are designed to foster an environment of collaboration and change, transparency and openness, and deliver on the sector’s net zero ambitions.

These recommendations include putting the consumer at the heart of organisational decision making, using integrated data sources at all stages of the value chain, and keeping whole systems models at the forefront when deploying new infrastructure.

Nigel Watson, Chief Innovation Officer at Northumbrian Water Group, said as we near the halfway mark on AMP7 (Asset Management Period), we are now starting to shape and share what our plan will be for AMP8.

"We have already set our own ambitious target to reach net zero by 2027," he said. "What is becoming clear is the need to collaborate on how this is achieved and how we understand and utilise the tools that will deliver on our bold environmental ambitions. The insights offered from open data are ultimately what will help us to drive the systemic responses to these challenges and help enable the transition to net zero in our industry.”


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