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
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.
This is a post-peer-review, pre-copyedit version of an article published in International Conference on Advanced Data Mining and Applications. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-69179-4_51