Abstract
Electricity load forecast is critical to grid safety. Various artificial intelligence (AI) models have been proposed to forecast the short-term load, but little research has been conducted to investigate the effect of environmental factors like extreme weather. We aim to identify the most accurate AI models, discuss their implications for grid safety, and analyze the performance of the most accurate AI models under hot and cold wave conditions. Based on the experiment, we use Mean absolute percentage error (MAPE) and daily percentage error as measurement methods to evaluate the accuracy and stability of the model. We observe that the Long-Short-Term Memory (LSTM) model outperforms XgBoosting and Support vector machines (SVM) to forecast load at the cost of unstable daily percentage errors when extreme weather conditions occur. The grid operators who need to develop more accurate forecasting models and discuss the implications of short-term load forecasting for grid safety can benefit from this finding.
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Guo, F., Wu, JZ., Pan, L. (2023). An Empirical Study of AI Model’s Performance for Electricity Load Forecasting with Extreme Weather Conditions. In: Yung, M., Chen, C., Meng, W. (eds) Science of Cyber Security . SciSec 2023. Lecture Notes in Computer Science, vol 14299. Springer, Cham. https://doi.org/10.1007/978-3-031-45933-7_12
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