Power Load Forecasting Using a Refined LSTM

The power load forecasting is based on historical energy consumption data of a region to forecast the power consumption of the region for a period of time in the future. Accurate forecasting can provide effective and reliable guidance for power construction and grid operation. This paper proposed a power load forecasting approach using a two LSTM (long-short-term memory) layers neural network. Based on the real power load data provided by EUNITE, a power load forecasting method based on LSTM is constructed. Two models, single-point forecasting model and multiple-point forecasting model, are built to forecast the power of next hour and next half day. The experimental results show that the mean absolute percentage error of the single-point forecasting model is 1.806 and the mean absolute percentage error of the multiple-points forecasting model of LSTM network is 2.496.

[1]  Qifa Xu,et al.  Short-Term Power Load Forecasting Based on Self-Adapting PSO-BP Neural Network Model , 2012, 2012 Fourth International Conference on Computational and Information Sciences.

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Yongli Wang,et al.  Short-term power load forecasting based on IVL-BP neural network technology , 2012 .

[4]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[5]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[6]  Alex Graves,et al.  Long Short-Term Memory , 2020, Computer Vision.

[7]  Wei Yanan,et al.  Short-term Power Load Combinatorial Forecast Adaptively Weighted by FHNN Similar-day Clustering , 2013 .

[8]  Afshin Fassihi,et al.  QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components. , 2010, European journal of medicinal chemistry.

[9]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[10]  Xiaohua Li,et al.  Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network , 2017, 2017 51st Annual Conference on Information Sciences and Systems (CISS).

[11]  Nitish Srivastava,et al.  Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.

[12]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[13]  Haiyan Lu,et al.  A case study on a hybrid wind speed forecasting method using BP neural network , 2011, Knowl. Based Syst..

[14]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[15]  Shanlin Yang,et al.  Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function , 2016 .

[16]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.

[17]  Yong Liu,et al.  Short-term power load forecasting based on SVM , 2012, World Automation Congress 2012.

[18]  Desheng Dash Wu,et al.  Power load forecasting using support vector machine and ant colony optimization , 2010, Expert Syst. Appl..