Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

Abstract

Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.

Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid

“…Reference [24] uses automatic coding to compress the load history data and constructs a prediction model based on multilayer gated recurrent unit (GRU) to analyze and predict the daily variation of power load. Reference [25] uses long short-term memory (LSTM) model to predict load power based on temperature history data and power history data. Reference [26] combines LSTM network and RNN network to realize effective prediction of industrial power load based on multilayer hybrid deep learning network.…”

“…In this study, reference [25] and reference [26] are used as comparison methods to realize the prediction and simulation experimental verification of the experimental data set, respectively. All methods realize the performance analysis under the same experimental scenario.…”

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

“…In a few cases, MI-ANN, WNN, GRU, DBN, RBM, ANFIS, and ART network approaches were applied to obtain better performances. Cascade NN, KNN-ANN [44], [48], [63], [65], [66], [73], [74], [75], [96], [109], [124], [129], [131], [133], [134], [137], [ [40], [42], [45] - [65], [67] - [73], [76] - [95], [97], [98], [101] - [103], [105] - [107], [110] - [119], [121], [123] - [126], [130] - [41], [42], [45] - [48], [52], [57], [61] - [63], [65], [69], [70], [72], [76],…”

Reliable Prediction on Emerging Energy Supply for National Sustainability and Stability: A Case Study on Coal Bed Gas Supply in China Based on the Dual-LSTM Model

“…From the perspective of emerging energy supply which features expected distribution uncertain and high noise, deep learning model is desirable for whose regression analyse [33][34]. Nevertheless, the application of deep learning algorithms on high noise or linear time series prediction were rarely studied due to that the deep learning algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods [35][36][37]. Moreover, overfitting frequently occurs while specifically predicting emerging energy supply [38][39][40].…”