scholarly journals Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2188
Author(s):  
Jieyun Zheng ◽  
Linyao Zhang ◽  
Jinpeng Chen ◽  
Guilian Wu ◽  
Shiyuan Ni ◽  
...  
Keyword(s):  
Neural Network ◽  
Correlation Analysis ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Energy Systems ◽  
Short Term ◽  
Term Memory ◽  
Multiple Loads ◽  
Input Feature ◽  
Multiple Load

With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be the primary premise for the optimal operation of integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network forecasting model. First, Copula correlation analysis is used to conduct correlation analysis on multiple loads and various influencing factors. The influencing factors that have a great correlation with multiple loads were screened out as the input feature set of the model to eliminate the influence of interfering factors. Then, a deep bidirectional long and short-term memory neural network was constructed. Combined with the input feature set screened by the Copula correlation analysis method, the useful information contained in the historical data was more comprehensively learned from the forward and backward directions for training and forecasting. Through the actual calculation example analysis and comparison with other models, the forecasting accuracy of the method presented in this paper was improved to a certain extent.

scholarly journals Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Hla U May Marma ◽  
M. Tariq Iqbal ◽  
Christopher Thomas Seary
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour.  In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

scholarly journals Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

2019 ◽  
Vol 11 (4) ◽  
pp. 987 ◽  
Author(s):  
Sana Mujeeb ◽  
Nadeem Javaid ◽  
Manzoor Ilahi ◽  
Zahid Wadud ◽  
Farruh Ishmanov ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Electricity Market ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Energy Market ◽  
Short Term ◽  
Term Memory ◽  
Forecasting Methods ◽  
Long Short Term Memory

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

scholarly journals Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 130
Author(s):  
Gwo-Ching Liao
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Power Systems ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Short Term Load Forecasting ◽  
Long Short Term Memory

Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the MAPE by about 20% to 50% and RMSE by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system.

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