Cost-oriented load forecasting

• Published in: Electric power systems research 2022-04, Vol.205, p.107723, Article 107723
• Main Authors: Zhang, Jialun, Wang, Yi, Hug, Gabriela
• Format: Article
• Language: English
• Subjects: Asymmetric loss function; Cost analysis; Data analytics; Economic dispatch; Economic forecasting; Electricity distribution; Errors; Forecasting; Load; Load forecasting; Operations management; Regression models; Unit commitment
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2021.107723

•Propose a generalized framework to quantify losses with respect to different forecasting errors.•Derive a fully differentiable cost-oriented loss function on the generated loss data.•Integrate the cost-oriented loss functions with MLR and ANN models.•Verify the effectiveness of proposed method based on a DAED-IPB problem integrated with BESS. Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the Mean Square Error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost caused by forecasting errors in the real power system is probably neither symmetric nor quadratic. To tackle this issue, this paper proposes a generalized cost-oriented load forecasting framework. Specifically, how to obtain a differentiable loss function that reflects real cost and how to integrate the loss function with regression models are studied. The economy and effectiveness of the proposed load forecasting method are verified by the case studies of an optimal dispatch problem that is built on the IEEE 30-bus system and the open load dataset from the Global Energy Forecasting Competition 2012(GEFCom2012).