An Electricity Load Forecasting Approach Combining DBN-Based Deep Neural Network and NAR Model for the Integrated Energy Systems

2019 ◽  
Author(s):  
Pan Yi ◽  
Zheng Jianyong ◽  
Yang Yun ◽  
Zhu Rui ◽  
Zhou Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Energy Systems ◽  
Integrated Energy Systems ◽  
Electricity Load ◽  
Electricity Load Forecasting

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.

scholarly journals Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 804-838
Author(s):  
Manogaran Madhiarasan ◽  
Mohamed Louzazni
Keyword(s):  
Neural Network ◽  
Power System ◽  
Multilayer Perceptron ◽  
Load Forecasting ◽  
Consumer Information ◽  
Fine Tuning ◽  
Atmospheric Parameters ◽  
Proposed Model ◽  
Electricity Load ◽  
Electricity Load Forecasting

With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10-05 for Dataset 1 and MSE of 4.0142 × 10-07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10-07 for Dataset 1, and MSE of 1.0425 × 10-08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable.

scholarly journals ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION

2020 ◽  
Vol 10 (2) ◽  
pp. 200-205
Author(s):  
Isaac Adekunle Samuel ◽  
Segun Ekundayo ◽  
Ayokunle Awelewa ◽  
Tobiloba Emmanuel Somefun ◽  
Adeyinka Adewale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting
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