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Energy Supply Innovation

Energy Supply Innovation

Load Forecasting

Load Forecasting

AI-Driven Load Forecasting
for Power
Generation Optimisation

AI-Driven Load Forecasting for Power Generation Optimisation

A key driving force in the decarbonisation journey is the optimisation of fuel use while we continue to integrate renewable sources in our energy mix. Utility companies count on various external factors and internal resources to precisely perform power load forecasting. By incorporating digital capability, millions of historical load profile and external data points can be analysed in real-time with dynamic forecasting to maximise operational efficiency and optimise the power supply and demand balance at the grid level.

Traditional load forecasting is a complex process that involves analysing a vast amount of historical energy data and external factors. This method has its limitations and is often based on professional experience of system planners who use various energy profile models to make their judgements. However, this manual process by experienced and professional system planners does not facilitate knowledge transfer. Digitalising the energy profile modelling and forecasting process is an effective and scientific way to minimise fuel consumption and improve energy efficiency.

How does it work?

How does it work?

We believe an ideal load forecasting solution shall leverage big data technologies and machine learning algorithms to analyse numerous data points against historical profiles. This analysis can then be used to perform energy modelling in real-time to optimise efficiency in the power generation phase. An AI-based system load and demand forecasting engine can also help to perform forecasting at a high level of granularity and accuracy versus manual human forecasts.

An effective load forecasting solution shall also enable planners to accurately perform short-term load forecasting for next day demand and recommend the most suitable profile, empowering planners to evaluate and plan power generation effectively.

Features

Features

The solution shall include an AI-enabled forecast engine that analyses historical energy load profiles against real-time consumption and weather factors. Based on possible scenarios, the system can generate multiple load profiles within a short timeframe.

Not only can the algorithm provide multiple load forecast profiles, but it can also recommend the most suitable load profile for the planners. The final decision is stored automatically for continuous machine learning to finetune the forecast mechanism over time.

The engine shall continuously process real-time data to forecast energy demand at a granular level, down to 15-min interval, so planners may make timely adjustments to secure a balanced power grid.

Benefits

Benefits

Increase Forecasting Accuracy

Improve accuracy and efficiency with data-driven decision making

Enhance Efficiency

Minimise time and resources spent in manual calculation and lengthy approval process

Transform Knowledge into Action

Enhance data transparency and improve expert knowledge transfer