Deep neural network based demand side short term load forecasting

By Seunghyoung Ryu, Jaekoo Noh, Hongseok Kim. IEEE International Conference on Smart Grid Communications, 2016

392 citations recorded by the source corpus.

In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based load forecasting models, and apply them to demand side empirical load database. DNNs are trained by two different ways: pre-training restricted Boltzmann machine and using rectified linear unit without pre-training. DNN forecasting models are trained by individual customer's electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with shallow neural network (SNN), and double seasonal Holt-Winters (DSHW) model. The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. The results show that DNNs exhibit accurate and robust forecasts compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN, and 9% and 29% compared to DSHW.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ab337a5d5bfcf4af6b11e2e28754d8b0cf996650

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Citations in the corpus

On-Line Building Energy Optimization Using Deep Reinforcement Learning (2017, methodology): “[28] used the Long Short-Term Memory (LSTM) method for build-”
Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, background): “Recently, Neural Network (NN) is widely used to forecast the wholesale electricity prices [44] and CU energy demands [45,46].”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, methodology): “[91] proposed a deep neural network (DNN) load forecasting method for short term prediction at the load side which demonstrated as high as 29% less error compared to existing systems such as shallow neural network (SNN), and double seasonal Holt-Winters (DSHW).”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, methodology): “Short-term load forecasting (STLF) has attracted more attention in the smart grid, microgrids and buildings and because of its usefulness for demand side management, energy consumption, energy storage operation, peak load anticipation, energy shortage risk reduction, etc. [14].”
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network (2019, neutral): “[21]”
Short-Term Load Forecasting With Deep Residual Networks (2018, background): “machines (RBM) and feed-forward neural networks with multiple layers in forecasting of demand side loads and natural gas loads [19], [20].”