Learning Vector Quantization Neural Network (LVQNN): Can it be implemented on the Forecasting Electrical Loads?

International Conference on SMART CITY Innovation 2018

Dharma, A.^1 ; Mataram, N.K.^2 ; Antara, I.N.G.^3

Department of Electrical and Computer Engineering, Faculty of Engineering, Udayana University, Jalan Raya Kampus UNUD, Kampus Bukit Jimbaran, South Kuta, Bali

80361, Indonesia^1

Department of Civil Engineering, Faculty of Engineering, Udayana University, Bali, Jalan Raya Kampus UNUD, Kampus Bukit Jimbaran, South Kuta, Bali, Denpasar

80361, Indonesia^2

Department of Mechanical Engineering, Faculty of Engineering, Udayana University, Bali, Jalan Raya Kampus UNUD, Kampus Bukit Jimbaran, South Kuta, Bali, Denpasar

80361, Indonesia^3

关键词: Classification algorithm;    Data classification;    Electrical power;    Forecasting techniques;    Learning Vector Quantization neural networks;    Mean absolute percentage error;    Price fluctuation;    Short term load forecasting;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/248/1/012027/pdf
DOI  :  10.1088/1755-1315/248/1/012027

来源: IOP

【 摘 要 】

Short-term load forecasting is an important activity in planning the operation of the electric power system to estimate the load conditions of the following days and the results may help in the decision making. The imbalance in electrical power between the supply side and the demand side may lead blackouts on the consumer side. Consequently, the generating unit must be operated to meet its load requirements. Various techniques and methods are used to short-term load forecasting. Learning vector quantization neural network (LVQNN) is a classification algorithm that is superior in classifying digital images. Based on these considerations this research aimed to develop LVQNN to forecast short-term electricity peak loads. The idea was used as a reference on the discovery of architectural data classification processes that resembled forecasting techniques. LVQNN development was carried out by adjusting the sample data to the LVQNN architecture. First-distinct sample data were used to obtain the weight vector, then the remaining data from the distinct data process were divided into training data and testing data. By developing the new concept of LVQNN into forecasting technique, the preliminary research was conducted on 11 commodities to predict price fluctuations that have relatively precise forecasting values when implemented in the population. Based on these results, this algorithm will be developed to forecast the fluctuation of electrical load to provide better results in the upcoming research. It is expected to perform with better accuracy results to indicate that the mean absolute percentage error (MAPE) of the predicted values of loads (in MW) will close to the actual loads.