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

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai’an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.

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

Jurnal Teknologi ◽

10.11113/jt.v69.3109 ◽

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.

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