Utilization of Artificial Neural Networks for Precise Electrical Load Prediction

TY - JOUR

T1 - Utilization of Artificial Neural Networks for Precise Electrical Load Prediction

AU - Pavlatos, Christos

AU - Makris, Evangelos

AU - Fotis, Georgios

AU - Vita, Vasiliki

AU - Mladenov, Valeri

N1 - Publisher Copyright: © 2023 by the authors.

PY - 2023/6

Y1 - 2023/6

N2 - In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature to tackle this issue. This paper introduces an effective framework, coded in Python, that can forecast future electrical load based on hourly or daily load inputs. The framework utilizes a recurrent neural network model, consisting of two simpleRNN layers and a dense layer, and adopts the Adam optimizer and tanh loss function during the training process. Depending on the size of the input dataset, the proposed system can handle both short-term and medium-term load-forecasting categories. The network was extensively tested using multiple datasets, and the results were found to be highly promising. All variations of the network were able to capture the underlying patterns and achieved a small test error in terms of root mean square error and mean absolute error. Notably, the proposed framework outperformed more complex neural networks, with a root mean square error of 0.033, indicating a high degree of accuracy in predicting future load, due to its ability to capture data patterns and trends.

AB - In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature to tackle this issue. This paper introduces an effective framework, coded in Python, that can forecast future electrical load based on hourly or daily load inputs. The framework utilizes a recurrent neural network model, consisting of two simpleRNN layers and a dense layer, and adopts the Adam optimizer and tanh loss function during the training process. Depending on the size of the input dataset, the proposed system can handle both short-term and medium-term load-forecasting categories. The network was extensively tested using multiple datasets, and the results were found to be highly promising. All variations of the network were able to capture the underlying patterns and achieved a small test error in terms of root mean square error and mean absolute error. Notably, the proposed framework outperformed more complex neural networks, with a root mean square error of 0.033, indicating a high degree of accuracy in predicting future load, due to its ability to capture data patterns and trends.

KW - electrical load

KW - medium-term forecasting

KW - recurrent neural network

KW - short-term forecasting

UR - http://www.scopus.com/inward/record.url?scp=85163625677&partnerID=8YFLogxK

U2 - 10.3390/technologies11030070

DO - 10.3390/technologies11030070

M3 - Journal article

AN - SCOPUS:85163625677

SN - 2227-7080

VL - 11

JO - Technologies

JF - Technologies

IS - 3

M1 - 70

ER -