A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

Abstract

Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression, Artificial Neural Networks, Extreme Learning Machine and Support Vector Machine. These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs Convolution Neural Network and Gated Recurrent Units into a unified framework for accurate energy consumption prediction. The proposed framework has two major phases: (1) data refinement and (2) training, where the data refinement phase applies preprocessing strategies over raw data. In the training phase, CNN features are extracted from input dataset and fed in to GRU, that is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments. The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features' extraction potentials of CNNs and effectual gated structure of multi-layered GRU. The experimental evaluation over existing energy forecasting datasets reveal the better performance of our method in terms of preciseness and efficiency. The proposed method achieved the smallest error rate on individual Appliances Energy prediction and household electric power consumption datasets, when compared to other baseline models.

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

“…In order to highlight the powerful non-linear fitting ability, Since the amount of source training data is relatively large relative to the amount of task data, and the amount of data to be calculated is small, in order to test whether the amount of task data affects the migration result when the source training data is migrated based on the maximum mean difference contribution coefficient method, auxiliary sample data is introduced and set are 10 auxiliary sample batches, and the data of August 22, August (22)(23), August (22)(23)(24),..., August (22)(23)(24)(25)(26)(27)(28)(29)(30)(31) are taken as 10 sample data, the number of samples is 96,192,...,960 in sequence. Then take the data under different auxiliary samples as the target data, migrate data close to the target data distribution from the source data, calculate the MMD value of each auxiliary sample, the source data, and the migrated data respectively, and use the migration data of each auxiliary sample The TDBN-DNN model obtained after finetuning the network calculates the target task data, and the Gaussian kernel width control parameter = 2.…”

“…In [24], the authors proposed a method based on deep belief network to improve power system transient stability evaluation (TSA) The method of accuracy. Reference [25] proposed a joint calculation method of short-term electrical, thermal, and gas load based on deep structure multitask learning. So far, there is no relevant research on the application of deep learning in the calculation of network loss rate.…”

Short-Term Load Forecasting Using Recurrent Neural Networks With Input Attention Mechanism and Hidden Connection Mechanism

“…Sajjad et al achieved the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs CNN and GRU into a unified framework for accurate energy consumption prediction. Results show that features are extracted by CNN from input dataset and fed into GRU, which is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments [15]. However, the efficiency is also greatly reduced with complex convolution operation in CNN module.…”

“…CNN has achieved excellent performance in load forecasting due to its ability to effectively extract features. LSTM with CNN and GRU with CNN have been proposed to enhance the forecasting accuracy by using CNN to extract the features of the load values, respectively [14], [15]. Nevertheless, the training of RNNs with CNN is too slow owing to a large number of learn-able parameters in convolution operation.…”

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

“…ILF techniques are designed to eradicate extra energy creation and depletion and are reliable for energy optimization 2 . Such techniques facilitate in energy management for both the demand‐side (industrial sector and household buildings 3 ) and the providers (smart grids 4 ) by offering better creation and utilization options of energy.…”