Accurate prediction of spot price can provide beneficial guidance for market participants in trading strategies. In order to improve the long step spatial-temporal prediction accuracy of regional spot electricity price

a prediction framework using dual channel spatial-temporal transformer (ST-Former) based on seasonal trend decomposition is proposed. This prediction framework adapts the idea of decomposition

and obtain more predictable results including a stable trend part and a fluctuating seasonal part by untangling the entangled time patterns in regional spot electricity prices. On this basis

a ST-Former model based on the combination of variable Patch temporal attention and spatial self-attention is proposed

to explore the long step spatial- temporal features of regional spot electricity prices. Then

the ST-Former model is used to model the trend part and seasonal part

forming a dual channel model framework. Finally

the output results of the dual channel model framework are aggregated to obtain the final prediction of regional spot electricity prices. Taking the spot electricity price data of the Australian electricity market as an example

the effectiveness and superiority of the proposed prediction framework are verified by comparing with twelve prediction methods.