Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management

Kong, Xiangyu; Li, Chuang; Zheng, Feng; Wang, Chengshan

doi:10.1109/tpwrs.2019.2943972

Cited by 93 publications

(44 citation statements)

References 31 publications

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“…Deng et al [31] devised a deep multi-scale CNN (MSCNN) with time cognition and a selfdesigned time coding algorithm, which outperformed recursive multi-step LSTM, direct multi-step MSCNN and the direct multi-step gated CNN by MAPE of 34.73%, 14.22% and 19.05% respectively. An improved DBN especially for Demand Side Management was designed by Kong [32] for STLF, outperforming autoregressive integrated moving average (ARIMA), Least Square SVM and conventional DBM with MAPE and RMSE of 3.864 and 341.601 respectively. RNN and its different approaches are widely used for short-term residential load forecasting [33].…”

Section: ) Deep Learning (Dl)mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

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Section: ) Deep Learning (Dl)mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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“…The previous sub-sections have extracted the time-domain and frequency-domain features individually from two channels. This process is to obtain the time-frequency fusion features by using concatenation operation, as shown in (16), where () is flattening operation. The concatenated features will be processed by the next operation and fused in a linear mode for forecasting.…”

Section: B Feature Fusionmentioning

confidence: 99%

“…In which the hidden layer of DNN is shared, and transfer strategy was applied to forecast newly-built wind farms using the actual wind speed of old wind farms. Kong et al [16] proposed an improved deep brief network for loading forecasting with four kinds of data: weather data, load demand data and demand-side management (DSM) data. Ullah et al proposed a deep autoencoder to transfer the low-dimensional energy consumption data into high-level representations for analyzing consumption profiling using meter data [17].…”

Section: Introductionmentioning

confidence: 99%

Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting

et al. 2020

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Short-term power consumption forecasting plays a critical role in the process of building the smart grid. However, it is very challenging as the power consumption series has strong randomness and volatility. In this paper, the authors proposed a novel domain fusion deep model based on convolutional neural network (CNN), long short-term memory (LSTM), and discrete wavelet transform (DWT) to deal with this task accurately. The proposed deep model has two channels: raw power consumption and DWT. The raw power consumption channel corresponding to time-domain feature extraction while the DWT channel is frequency domain. They extract time-domain and frequency-domain features individually by using CNN. CNN extracted time-domain, and frequency-domain features are merged as time-frequency fusion features, which fully reflect the changing power consumption trend. The time-frequency fusion features are fed into LSTM to mining the features which have a long-time dependency. The comprehensive features are the fusion of time-domain and frequency-domain features with a long-time dependency, which are utilized for power consumption forecasting. The proposed method is evaluated on two public nature data sets related to power consumption with multiple metrics. The comparative experimental analysis has confirmed the state-of-theart performance of the proposed method for short-term power consumption forecasting.

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“…where Z is the partition function. Then, the model parameters of the visible and hidden layers can be updated using the derivative of L θ,s based on Bayesian statistics theory and Gibbs sampling [39], as shown below:…”

Section: B Deep Learning-based Point Prediction Module 1) Deep Belimentioning

confidence: 99%

A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

Zhang

2020

IEEE Access

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With the deregulation of the electric energy industry, accurate electricity price forecasting (EPF) is increasingly significant to market participants' bidding strategies and uncertainty risk control. However, it remains a challenging task owing to the high volatility and complicated nonlinearity of electricity prices. Aimed at this, a novel hybrid deep-learning framework is proposed for day-ahead EPF, which includes four modules: the feature preprocessing module, the deep learning-based point prediction module, the error compensation module, and the probabilistic prediction module. The feature preprocessing module is based on isolation forest (IF), and least absolute shrinkage and selection operator (Lasso), which is used to detect outliers and select the correlated features of electricity price series. The point prediction module combines the deep belief network (DBN), long-short-term memory (LSTM) neural network (RNN), and convolutional neural network (CNN), and is employed to extract complicated nonlinear features. The residual error between forecasting price and actual price can be reduced based on the error compensation module. The probabilistic prediction module based on quantile regression (QR) is used to estimate the uncertainty under various confidence levels. The PJM market data is employed in case studies to evaluate the proposed framework, and the results revealed that it has a competitive advantage compared with all of the considered comparison methods. INDEX TERMS Electricity market, day-ahead electricity price forecasting, feature preprocessing, deep learning, error compensation, probabilistic forecasting.

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Improved Deep Belief Network for Short-Term Load Forecasting Considering Demand-Side Management

Cited by 93 publications

References 31 publications

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting

A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

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