Machine learning

Window (computing)

Convolutional neural network

Engineering

Philosophy

Artificial intelligence

Economics

Power (physics)

Physics

Demand forecasting

Feature (linguistics)

Operations research

Term (time)

Random forest

Quantum mechanics

Electric power system

Operating system

Data mining

Voltage

Computer science

Electrical load

Linguistics

Sliding window protocol

Electrical engineering

Keynesian economics

Gradient boosting

Boosting (machine learning)

Industrial production

IEEE Transactions on Power Systems

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers' activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers' loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results.

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM