A Hybrid Model Based on Symbolic Regression and Neural Networks for Electricity Load Forecasting

2018 ◽  
Author(s):  
Ilias Dimoulkas ◽  
Lars Herre ◽  
Dina Khastieva ◽  
Elis Nycander ◽  
Mikael Amelin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hybrid Model ◽  
Load Forecasting ◽  
Symbolic Regression ◽  
Model Based ◽  
Electricity Load ◽  
Electricity Load Forecasting

scholarly journals A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Shubin Zheng ◽  
Qianwen Zhong ◽  
Lele Peng ◽  
Xiaodong Chai
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Load Forecasting ◽  
Forecast Model ◽  
Time Range ◽  
Simple Method ◽  
Load Forecast ◽  
Bayesian Neural Networks ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.

Short term electricity load forecasting using a hybrid model

Energy ◽  
2018 ◽  
Vol 158 ◽  
pp. 774-781 ◽  
Author(s):  
Jinliang Zhang ◽  
Yi-Ming Wei ◽  
Dezhi Li ◽  
Zhongfu Tan ◽  
Jianhua Zhou
Keyword(s):  
Hybrid Model ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Electricity Load Forecasting

scholarly journals Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1140 ◽  
Author(s):  
Xin Gao ◽  
Xiaobing Li ◽  
Bing Zhao ◽  
Weijia Ji ◽  
Xiao Jing ◽  
...  
Keyword(s):  
Feature Selection ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Short Term ◽  
Model Based ◽  
Electricity Load ◽  
Electricity Load Forecasting

Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

scholarly journals Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2467 ◽  
Author(s):  
Kailai Ni ◽  
Jianzhou Wang ◽  
Guangyu Tang ◽  
Danxiang Wei
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Data Preprocessing ◽  
Practical Applications ◽  
Avant Garde ◽  
Forecasting Models ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

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