Monday, February 11, 2019

Short-term Industrial Reactive Power Forecasting

Two years ago, I started collaborating with a team of Italian researchers. We had our first joint paper on short-term industrial load forecasting published at the 2017 ISGT-Europe. The complete story is HERE.

Since then, we've continued our collaboration. In this paper, we used the data from the same Italian factory. Now we focus on reactive power forecasting, a rarely touched topic in the load forecasting literature. 


Antonio Bracale, Guido Carpinelli, Pasquale De Falco, and Tao Hong, "Short-Term Industrial Reactive Power Forecasting," International Journal of Electrical Power & Energy Systems, vol.107, pp 177-185, May 2019 (ScienceDirect)

Short-term Industrial Reactive Power Forecasting

Antonio Bracale, Guido Carpinelli, Pasquale De Falco, and Tao Hong


Reactive power forecasting is essential for managing energy systems of factories and industrial plants. However, the scientific community has devoted scant attention to industrial load forecasting, and even less to reactive power forecasting. Many challenges in developing a short-term reactive power forecasting system for factories have rarely been studied. Industrial loads may depend on many factors, such as scheduled processes and work shifts, which are uncommon or unnecessary in classical load forecasting models. Moreover, the features of reactive power are significantly different from active power, so some commonly used variables in classical load forecasting models may become meaningless for forecasting reactive power. In this paper, we develop several models to forecast industrial reactive power. These models are constructed based on two forecasting techniques (e.g., multiple linear regression and support vector regression) and two variable selection methods (e.g., cross validation and least absolute shrinkage and selection operator). In the numerical applications based on real data collected from an Italian factory at both aggregate and individual load levels, the proposed models outperform four benchmark models in short forecast horizons.

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