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Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach

机译：利用深神经网络预测现代现货电价的空气温度的新变量：一种新方法

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The paper presents a way of creating three new, innovative variables based on air temperature to be used in forecasts of electricity demand and prices. The forecasting methods developed so far, especially in the area of energy prices, either did not use temperature data or were based on data that had not undergone pre-processing, which made it difficult for the model to use their potential. Newly developed variables have a linear relationship with the demand for electricity. This paper describes in detail the procedure for determining the parameters of new variables using the electricity market in Poland (a country in Central Europe) as a case study. The proposed approach allows both to avoid data clustering into different seasons and to precisely determine the temperatures at which the nature of the dependence with the demand for electricity changes. The validity of the proposed new variables in prognostic models has been confirmed by their use in deep neural networks. The proposed approach allows reducing the sMAPE by up to 15.3%. The designed new explanatory variables can be used not only in models based on artificial intelligence tools, but also in other forecasting methods that allow the use of exogenous inputs.
机译：本文提出了一种基于空气温度创造三种新的创新变量的方法，以便在电力需求和价格预测中使用。到目前为止开发的预测方法，特别是在能源价格领域，不使用温度数据或基于没有经过预处理的数据，这使得模型难以使用它们的潜力。新开发的变量具有与电力需求的线性关系。本文详细介绍了使用波兰（中欧国家）的电力市场来确定新变量参数的程序作为案例研究。所提出的方法允许避免数据聚类到不同的季节，并精确地确定与电力变化需求的依赖性质的温度。通过他们在深神经网络中的使用证实了预后模型中提出的新变量的有效性。所提出的方法允许将Smape降低至15.3％。设计的新的解释变量不仅可以在基于人工智能工具的模型中使用，也可以在其他预测方法中使用，允许使用外源性投入。

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