Abstract
Power demand forecasting is a critical task to achieve efficiency and reliability in the smart grid in terms of demand response and resource allocation. This paper proposes PowerLSTM, a power demand forecasting model based on Long Short-Term Memory (LSTM) neural network. We calculate the feature significance and compact our model by capturing the features with the most important weights. Based on our preliminary study using a public dataset, compared to two recent works based on Gradient Boosting Tree (GBT) and Support Vector Regression (SVR), PowerLSTM demonstrates a decrease of 21.80% and 28.57% in forecasting error, respectively. Our study also reveals that metering/forecasting granularity at once every 30 min can bring higher accuracy than other practical granularity options.
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Notes
- 1.
Given that the metering interval is fixed, the power is able to represent the power consumption.
- 2.
In our experiments, we eliminate the undefined MAPE caused by a zero actual value. However, the actual power consumption values in our dataset are scarcely zero.
References
Umass smart* dataset - 2017 release. http://traces.cs.umass.edu/index.php/Smart/Smart
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Acknowledgement
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under the Energy Programme and administrated by the Energy Market Authority (EP Award No. NRF2014EWT-EIRP002-040).
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Cheng, Y., Xu, C., Mashima, D., Thing, V.L.L., Wu, Y. (2017). PowerLSTM: Power Demand Forecasting Using Long Short-Term Memory Neural Network. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_51
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