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.

Electricity Load Forecasting using Hybrid of Multiplicative Double Seasonal Exponential Smoothing Model with Artificial Neural Network

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Osamah Basheer Shukur ◽  
Naam Salem Fadhil ◽  
Muhammad Hisyam Lee ◽  
Maizah Hura Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Load Forecasting ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Load Testing ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting

Electricity load forecasting often has many properties such as the nonlinearity, double seasonal cycles, and others those may be obstacles for the accuracy of forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model to forecast. These papers indicated that electricity load forecasting using DS exponential smoothing model has better fit. Using artificial neural network (ANN) as a modern approach may be used for superior fitted forecasting, since this approach can deal with the non-linearity components of load data. The purpose of this paper is to improve the electricity load forecasting by building the hybrid model that includes a double seasonal exponential smoothing with an artificial neural network. This hybrid model will study the double seasonal effects and non-linearity components together based on the electricity load data. The strategy of building this hybrid model is by entering ANN output as an input in double seasonal exponential smoothing model. The data sets are taken from three stations with different electricity load characteristics such as a residential, industrial and city center. The electricity load testing forecast of DS exponential smoothing-ANN hybrid model gave the most minimum mean absolute percentage error (MAPE) measurement comparing with the electricity load testing forecasts of DS exponential smoothing and ANN for all electricity load data sets. In conclusion, DS exponential smoothing-ANN hybrid model are the most fitted for every electricity load data which contains the double seasonal effects and non-linearity components.

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

scholarly journals Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Heng Guo ◽  
Chengwei Chai ◽  
Yanze Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Electricity Market ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Practical Applications ◽  
Load Pattern ◽  
Probabilistic Load ◽  
Reliability And Robustness ◽  
Quantile Loss

Residential load forecasting is important for many entities in the electricity market, but the load profile of single residence shows more volatilities and uncertainties. Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue. At first, a convolutional neural network with multiscale convolution is devised for extracting more behavioral features from the historical load sequence. In addition, a novel periodical coding method marks the model to enhance its ability of capturing regular load pattern. Then, features generated from both subnetworks are fused and fed into the forecasting model with an end-to-end manner. Besides, a globally differentiable quantile loss function constrains the whole network for training. At last, forecasts of multiple quantiles are directly generated in one shot. With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously. Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.

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
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