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Recurrent inception convolution neural network for multi short-term load forecasting
Highlights A new multi short-term load forecasting model named RICNN is proposed. The proposed model combines an RNN and a 1-D CNN of inception module. The proposed RICNN yields better forecasting performance than MLP, 1-D CNN and RNN. The proposed RICNN is verified by actual power consumption data collected from three industrial complexes in South Korea.
Abstract Smart grid and microgrid technology based on energy storage systems (ESS) and renewable energy are attracting significant attention in addressing the challenges associated with climate change and energy crises. In particular, building an accurate short-term load forecasting (STLF) model for energy management systems (EMS) is a key factor in the successful formulation of an appropriate energy management strategy. Recent recurrent neural network (RNN)-based models have demonstrated favorable performance in electric load forecasting. However, when forecasting electric load at a specific time, existing RNN-based forecasting models neither use a predicted future hidden state vector nor the fully available past information. Therefore, once a hidden state vector has been incorrectly generated at a specific prediction time, it cannot be corrected for enhanced forecasting of the following prediction times. To address these problems, we propose a recurrent inception convolution neural network (RICNN) that combines RNN and 1-dimensional CNN (1-D CNN). We use the 1-D convolution inception module to calibrate the prediction time and the hidden state vector values calculated from nearby time steps. By doing so, the inception module generates an optimized network via the prediction time generated in the RNN and the nearby hidden state vectors. The proposed RICNN model has been verified in terms of the power usage data of three large distribution complexes in South Korea. Experimental results demonstrate that the RICNN model outperforms the benchmarked multi-layer perception, RNN, and 1-D CNN in daily electric load forecasting (48-time steps with an interval of 30 min).
Recurrent inception convolution neural network for multi short-term load forecasting
Highlights A new multi short-term load forecasting model named RICNN is proposed. The proposed model combines an RNN and a 1-D CNN of inception module. The proposed RICNN yields better forecasting performance than MLP, 1-D CNN and RNN. The proposed RICNN is verified by actual power consumption data collected from three industrial complexes in South Korea.
Abstract Smart grid and microgrid technology based on energy storage systems (ESS) and renewable energy are attracting significant attention in addressing the challenges associated with climate change and energy crises. In particular, building an accurate short-term load forecasting (STLF) model for energy management systems (EMS) is a key factor in the successful formulation of an appropriate energy management strategy. Recent recurrent neural network (RNN)-based models have demonstrated favorable performance in electric load forecasting. However, when forecasting electric load at a specific time, existing RNN-based forecasting models neither use a predicted future hidden state vector nor the fully available past information. Therefore, once a hidden state vector has been incorrectly generated at a specific prediction time, it cannot be corrected for enhanced forecasting of the following prediction times. To address these problems, we propose a recurrent inception convolution neural network (RICNN) that combines RNN and 1-dimensional CNN (1-D CNN). We use the 1-D convolution inception module to calibrate the prediction time and the hidden state vector values calculated from nearby time steps. By doing so, the inception module generates an optimized network via the prediction time generated in the RNN and the nearby hidden state vectors. The proposed RICNN model has been verified in terms of the power usage data of three large distribution complexes in South Korea. Experimental results demonstrate that the RICNN model outperforms the benchmarked multi-layer perception, RNN, and 1-D CNN in daily electric load forecasting (48-time steps with an interval of 30 min).
Recurrent inception convolution neural network for multi short-term load forecasting
Kim, Junhong (author) / Moon, Jihoon (author) / Hwang, Eenjun (author) / Kang, Pilsung (author)
Energy and Buildings ; 194 ; 328-341
2019-04-19
14 pages
Article (Journal)
Electronic Resource
English
Recurrent inception convolution neural network , Deep learning , Recurrent neural network , Convolution neural network , Load forecasting , ANN , Artificial Neural Network , ARIMA , Autoregressive Integrated Moving Average , CNN , Convolution Neural Network , CRBM , Conditional Restricted Boltzmann Machine , DL , Deep Learning , DNN , Deep Neural Network , DT , Decision Tree , ELM , Extreme Learning Machine , EMS , Energy Management System , ESS , Energy Storage System , FCRBM , Factored Conditional Restricted Boltzmann Machine , GA-ANFIS , Genetic Algorithm–-Adaptive Network-based Fuzzy Inference System , GRNN , Generalized Regression Neural Network , HVAC , Heating, Ventilation, and Air Conditioning , ICT , Information & Communication Technology , IoT , Internet of Things , IPSO-ANN , Improved Particle Swarm Optimization-Artificial Neural Network , KEPCO , Korea Electric Power Corporation , KMA , Korea Meteorological Office , LSTM , Long Short-Term Memory , LTLF , Long-Term Load Forecasting , MAPE , Mean Absolute Percentage Error , MLP , Multilayer Perception , MLR , Multiple Linear Regression , MSE , Mean Square Error , MTLF , Mid-Term Load Forecasting , MWD , Multi-resolution Wavelet Decomposition , NLP , Natural Language Processing , PDRNN , Pooling-based Deep Recurrent Neural Network , PSO , Particle Swarm Optimization , PV , Photovoltaic , RBM , Restricted Boltzmann Machine , ReLU , Rectified Linear Unit , RF , Random Forest , RICNN , Recurrent Inception Convolution Neural Network , RMSE , Root Mean Square Error , RNN , Recurrent Neural Network , SFOA , The Fruit Fly Optimization Algorithm with Decreasing Step Size , SNN , Shallow Neural Network , SRWNN , Self-Recurrent Wavelet Neural Network , STLF , Short-Term Load Forecasting , SVR , Support Vector Regression , S2S , Sequence to Sequence , VSTLF , Very Short-Term Load Forecasting , WNN , Wavelet Neural Network , 1-D CNN , 1-Dimensional Convolution Neural Network
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