Energy consumption

Quantum mechanics

Engineering

Statistics

Scheduling (production processes)

Power (physics)

Computer science

Mathematical optimization

Physics

Mean absolute percentage error

Mathematics

Long short term memory

Operations management

Artificial intelligence

Deep learning

Term (time)

Artificial neural network

Electrical load

Mean squared error

Convolutional neural network

Recurrent neural network

Machine learning

Electrical engineering

IEEE Access

Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.

On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach