Recurrent inception convolution neural network for multi short-term load forecasting

Kim, Junhong; Moon, Jihoon; Hwang, Eenjun; Kang, Pilsung

doi:10.1016/j.enbuild.2019.04.034

Cited by 173 publications

(85 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiments show that this system reduces a significant amount of electric energy consumption at ratio 46.79%. Kim et al [29] proposed a recurrent inception convolution neural network (RICNN) to predict the electric energy consumption (48-time steps with an interval of 30 min). They combined RNN and 1-D convolution inception module to help calibrate the prediction time and the hidden state vector values calculated from the nearby time steps.…”

Section: Related Workmentioning

confidence: 99%

“…Electric energy consumption prediction (EECP), a multivariate time series forecasting issue, is an interesting issue that needs to be addressed for stable power supply. In recent years, there are many approaches proposed to predict the electric energy consumption [19][20][21][22][23][24][25][26][27][28][29] from various datasets. In 2012, Hebrail and Berard released the IHEPC dataset [30] on UCI Machine Learning Repository collected from an individual house in France.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

et al. 2019

Applied Sciences

Self Cite

194

View full text Add to dashboard Cite

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

et al. 2019

Applied Sciences

Self Cite

194

View full text Add to dashboard Cite

show abstract

“…The model was evaluated using the electric energy consumption data of university campuses and the results showed that it gave a better performance than the proposed model in [33]. In [35], we proposed a recurrent inception convolution neural network (RICNN) that combines recurrent neural networks (RNN) and 1-dimensional convolutional neural networks (CNN) to forecast multiple short-term electric loads (48 time steps with an interval of 30 min). A 1-D convolution inception module was used to calibrate the prediction time and hidden state vector values calculated from nearby time steps.…”

Section: Our Previous Workmentioning

confidence: 99%

“…However, since the performance of AE heavily depends on the size of the training set, it is challenging to show excellent performance if there is not enough quantity of data. In [35], we proposed the RICNN model. However, the RICNN, which purposed a probabilistic approach, is a different purpose because we focus on day-ahead point load forecasting.…”

Section: Our Previous Workmentioning

confidence: 99%

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

et al. 2020

Self Cite

View full text Add to dashboard Cite

Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.

show abstract

“…Therefore, we use 81 nodes for our forecasting model. In addition, we used Xavier initialization 55,56 to sort initial weights for individual inputs in a neuron model. When constructing an ANN model, two important hyperparameters are the learning rate and learning epoch.…”

Section: Other Hyperparameters Tuningsmentioning

confidence: 99%

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Moon

Park

Rho

et al. 2019

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

show abstract

Recurrent inception convolution neural network for multi short-term load forecasting

Cited by 173 publications

References 52 publications

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Contact Info

Product

Resources

About