A Review of Deep Learning Methods Applied on Load Forecasting

Almalaq, Abdulaziz; Edwards, George

doi:10.1109/icmla.2017.0-110

Cited by 201 publications

(93 citation statements)

References 15 publications

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“…A method based on least absolute shrinkage and selection (LASSO) is proposed to adaptively explore sparsity in historical data and leverage predictive relationship among different residences, and its low computational complexity and high accuracy are verified by experiments. Recently, deep learning (DL) has become a research hotspot of artificial intelligence applications in many fields due to its powerful feature extraction and fitting capabilities [15]. Abbas et al [16] proposed a unique and improved nonlinear autoregressive neural network with external input-(NARXNN-) based recurrent load forecaster using a lighting search algorithm (LSA).…”

Section: Related Workmentioning

confidence: 99%

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

et al. 2020

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Residential load forecasting is important for many entities in the electricity market, but the load profile of single residence shows more volatilities and uncertainties. Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue. At first, a convolutional neural network with multiscale convolution is devised for extracting more behavioral features from the historical load sequence. In addition, a novel periodical coding method marks the model to enhance its ability of capturing regular load pattern. Then, features generated from both subnetworks are fused and fed into the forecasting model with an end-to-end manner. Besides, a globally differentiable quantile loss function constrains the whole network for training. At last, forecasts of multiple quantiles are directly generated in one shot. With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously. Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.

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Section: Related Workmentioning

confidence: 99%

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

et al. 2020

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“…Artificial neural network (ANN) has always been one of the primary solutions for short‐term load forecasting. The latest development of neural networks, especially the deep learning method, has had a tremendous impact in the fields of computer vision, natural language processing, and speech recognition . Researchers fuse their understanding of different tasks into specific network structures rather than using a fixed shallow neural network structure.…”

Section: Artificial Neural Network Approaches and Model For Short‐termentioning

confidence: 99%

“…The latest development of neural networks, especially the deep learning method, has had a tremendous impact in the fields of computer vision, natural language processing, and speech recognition. [45][46][47][48][49] Researchers fuse their understanding of different tasks into specific network structures rather than using a fixed shallow neural network structure. Different building blocks, including CNN 50,51 and LSTM, make deep neural networks highly flexible and efficient.…”

Section: Artificial Neural Network Approaches and Model For Short-termentioning

confidence: 99%

An effective deep learning neural network model for short‐term load forecasting

Wang

et al. 2020

Concurrency and Computation

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Summary Energy load forecasting plays an important role in the smart grid, which can affect the promoting energy production and consumption decision‐making processes. In this paper, the state‐of‐the‐art deep learning (DL) neural models are used in the short‐term load forecasting, including the multilayer perceptron (MLP), the convolutional neural network (CNN), and the long short‐term memory (LSTM). A novel loss function is proposed for the load forecasting, and two commonly used benchmarks are used to verify the validity of the proposed function. The simulation results show that the mean absolute percentage error (MAPE) of the proposed loss function is 19.63% lower than cross‐entropy and 2.34% lower than mean absolute error (MAE). We compared the mentioned neural networks in different aspects, and the results show that in energy load forecasting, CNN has superior performance than MLP and LSTM in terms of high accuracy and robustness to weather changes.

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“…scientific research assistants and in decision making [11]. In DL, computational models of multiple layers of processing are allowed to study and represent data with multi-level abstraction that mimics the working of brain in recognizing and understanding multimodal information, and therefore it completely captures complex structures of large-scale data.…”

Section: Environmentmentioning

confidence: 99%

Deep Learning: A Vision for Computer

Reen¹

2019

IJRASET

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Artificial intelligence (AI) is countered to be one of the most trusted techniques to cope with variety of issues. Researchers are delving deeper by using the techniques of AI, such as Machine learning (ML) and deep learning (DL). ML has attained a high attraction in the industry and it is utilized by many applications. Due to drastic increase in data, these techniques are becoming popular amongst the researchers. Long with this, deep learning is the branch of ML which outperformed the conventional techniques of machine learning. This paper presents the brief account on ML and DL. It reviewed how machine and deep learning are utilized and perform different operations. Along with this, a literature survey is presented on the basis of three different domains: Security, Health Management and Big data. This paper gives an overview on Machine learning and deep learning along with the work proposed in this domain.

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A Review of Deep Learning Methods Applied on Load Forecasting

Cited by 201 publications

References 15 publications

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

An effective deep learning neural network model for short‐term load forecasting

Deep Learning: A Vision for Computer

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