Table 4.

DBN in economics

Article	Aim of study	Specific approach	Benchmark methods for comparison	Superiority of the proposed method
(Gensler et al., 2016)	Forecast the energy output of solar power plants	DBN, AE, LSTM	Multilayer perceptrons, physical models	Superior forecasting performance
(Cui et al., 2020)	Predictive control for ultrasupercritical power plant	DBN, Economic model predictive control	Subspace model identification	Performs better in terms of economic performance and tracking performance
(Rao et al., 2020)	Detect and classify the fault signal in power distribution system	DBN	SVM, quadratic SVM, RBF SVM, polynomial SVM, MLP SVM, LM-NN, GD-NN	Effectively detects and classifies the fault signal
(Huan et al., 2020)	Forecast short-term load of integrated energy systems	DBN, BP, multitask regression layer	SVR, ARIMA, BPNN	Learns better features and improves the forecasting accuracy
(Shi et al., 2019)	Forecast very short-term bus load	Phase space reconstruction, DBN	PSR-NN, DBN, ARIMA, NN, LSTM, PSR-DBN (no tuning)	Higher prediction accuracy and better adaptability

Note: LM-NN (Levenberg-Marquardt Neural Network), GD-NN (Gradient Descent Neural Network), BPNN (Back Propagation Neural Network), SVR (Support Vector Regression), ARIMA (Autoregressive Integrated Moving Average Model,), PSR-NN (Phase Space Reconstruction Neural Network), PSR-DBN (Phase Space Reconstruction Deep Belief Network)