A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Wu, Linlin; Chun, Kong; Hao, Xiaohong; Chen, Wei

doi:10.1155/2020/1428104

Cited by 103 publications

(67 citation statements)

References 33 publications

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“…In recent years, the data-driven, model-free artificial intelligence algorithms have shown great development prospects in the power forecasting field. Random forest [13,14], BP neural network [15], support vector machine [16], long short-term memory network [17], and convolutional neural network [18] have been used in the power forecasting by the researchers. For example, in [19], a short-term load forecasting method based on the combination of fuzzy time series (FTS) and convolutional neural network (CNN) is proposed.…”

Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Jiang

Xiong

2021

Advances in Civil Engineering

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The rapid increase of the number of electric vehicles (EVs) has posed great challenges to the safe operation of the distribution network. Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. Correspondingly, the residential distribution points, EV charging stations, and renewable plants are regarded as the edge nodes. Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. Finally, the charging station formulates the personalized charging scheduling strategies according to EV’s charging plans and the charging demands in order to follow the supply plan. The simulation results show that the load peak-to-valley difference and standard deviation of the proposed algorithm are reduced by 30.13% and 16.94%, respectively, compared with the disorderly charging and discharging behavior, which has verified the effectiveness and feasibility of the proposed method.

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Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Jiang

Xiong

2021

Advances in Civil Engineering

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“…These models are subcategories of the deep learning structure. The hybrid model GRU-CNN combines the "gated-recurrent-unit" (GRU) and the "convolutional-neural network" (CNN), in which the GRU is applied to extract time-series feature and the CNN is used to extract other high-dimensional feature data [131]. DCNN has the share-weight architecture and can operate with minimum pre-processing on the translation of features.…”

Section: Other Ann Structuresmentioning

confidence: 99%

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

et al. 2021

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This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

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“…Wu et al [42] One min Gansu, China MAPE = 2.8839% CNN, GRU Jin et al [43] One hour Queensland, Australia MAPE = 0.7653% VMD, BEGA, LSTM Nie et al [44] One hour Australia MAPE = 0.7280% CEEMD, SSA, RBF, ELM, GRNN Heydari et al [45] One hour America MAPE = 0.8657% VMD, GRNN, GSA Shao et al [46] Half day PJM MAPE = 3.13% LSTM, CAE, K-means Bedi et al [47] One day Himachal Pradesh, India MAPE = 3.04% VMD, ACA, EVM-S, LSTM Deng et al [48] One day Yichun, China MAPE = 2.057% VMD, DBN Mansoor et al [49] One day Milan, Italy MAPE = 2.937% FFNN, ESN Yin et al [50] One day Guangxi, China MAPE = 1.89% MTCN Kong et al [51] One day Tianjin, China MAPE = 3.104% DMD, EVCM, SVR…”

Section: Authors and Ref Forecast Horizon Data Sources Evaluation Indmentioning

confidence: 99%

An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

Shen

Deng

et al. 2021

Sustainability

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Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity load and determine the data to be input into the model. The variational mode decomposition (VMD) algorithm was used to decompose the electricity load raw-data into a set of relatively stable modes named intrinsic mode functions (IMFs). According to the time distribution and time lag determined using the ACF, the input of the model was reshaped into a 24 × 7 × 8 matrix M, where 24, 7, and 8 represent 24 h, 7 days, and 8 IMFs, respectively. In the load forecasting part, a two-dimensional convolutional neural network (2D-CNN) was used to extract features from the matrix M. The improved reshaped layer was used to reshape the extracted features according to the time order. A temporal convolutional network was then employed to learn the reshaped time-series features and combined with the fully connected layer to complete the prediction. Finally, the performance of the model was verified in the Eastern Electricity Market of Texas. To demonstrate the effectiveness of the proposed model data processing and load forecasting, we compared it with the gated recurrent unit (GRU), TCN, VMD-TCN, and VMD-CNN models. The TCN exhibited better performance than the GRU in load forecasting. The mean absolute percentage error (MAPE) of the TCN, which was over 5%, was less than that of the GRU. Following the addition of VMD to the TCN, the basic performance of the model was 2–3%. A comparison between the SELNet model and the VMD-TCN model indicated that the application of a 2D-CNN improves the forecast performance, with only a few samples having an MAPE of over 4%. The model’s prediction effect in each season is discussed, and it was found that the proposed model can achieve high-precision prediction in each season.

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A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Cited by 103 publications

References 33 publications

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

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