Governments worldwide are offering subsidies to promote the transition to renewable energy, signaling a global shift towards embracing a more sustainable energy future. While there is much excitement around adopting renewable energy and targets being set for a sustainable future, it is imperative to address the problems that arise from the lack of orderly integration of DERs. Energy mismanagement is one of the key issues in peer to peer energy trading. Here’s how we combine the power of artificial intelligence with energy trading for enhanced energy clarity. 

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AI-based Hybrid Weighted Energy Forecasting Algorithm (HWEFA) for look ahead peer to peer energy trading

Powerledger’s new AI-based Hybrid Weighted Energy Forecasting Algorithm (HWEFA) offers informed decision making backed by comprehensive insights into your energy, so that you achieve a sustainable and efficient energy profile by optimising your energy consumption and production.

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Why HWEFA?

Energy management has long been plagued by a lack of clarity, leading to inefficiencies and unnecessary energy wastage. Our AI-based Hybrid Weighted Energy Forecasting Algorithm (HWEFA) tackles this issue and offers informed decision making backed by comprehensive insights into your energy, so that you achieve, 

• Optimised energy consumption and production.
• Streamlined decision-making with real-time data.
• Achieve a sustainable and efficient energy profile.
• Accurate prediction for each user with dynamic calculation.

How does it work?

Our Hybrid Weighted Energy Forecasting Algorithm (HWEFA) aims to enhance energy clarity to simplify and optimise your energy management.

HWEFA sets itself apart by not only relying on historical data but also using real-time energy information for day-ahead forecasting in P2P energy trading. Experience the power of AI for comprehensive forecasting by source (Load, Battery and Solar PV Generation)

Admins can streamline energy management with features that help them,        

• Create, manage, and fine-tune typical profile curves.
• Easily import utility-provided data or use algorithms to generate profiles.
• Simplify the process of setting default curves for trading groups.

HWEFA simplifies energy forecasting by storing typified curves as normalised values, making it easy to compare and analyse with the use of historic data. The actual participant curves are dynamically calculated, ensuring accurate predictions for each user. Figure 1 illustrates the complete forecasting process through six distinct stages.

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Ready to explore the potential of HWEFA? Book a demo now to experience firsthand the power of AI-driven forecasting in P2P energy trading.