Discrete cosine transform-based predictive model extended in the least-squares sense for hourly load forecasting

作者：Yang, Zong-Chang^*

来源：IET Generation Transmission & Distribution, 2016, 10(15): 3930-3939.

DOI：10.1049/iet-gtd.2016.0689

• load forecasting
• electricity supply industry
• power system economics
• power system control
• discrete cosine transforms
• regression analysis
• predictive control
• time series
• discrete cosine transform-based predictive model
• hourly load forecasting
• electric load forecasting
• economic operation
• electric power industry
• Fourier-related computational technique
• signal processing
• image processing
• optimal decorrelation
• energy compaction
• time-series analysis
• DCT-based predictive model

Electric load forecasting is essential to economic operation and power system control in the electric power industry. Modelling and forecasts for different time horizons are of increasingly importance for different operations. Discrete cosine transform (DCT) is one Fourier-related computational technique. It is widely used in signal and image processing for its remarkable characteristics on optimal decorrelation and energy compaction. From time-series analysis perspective, this study proposes a DCT-based predictive model for forecasting hourly load movement. Based on finite hourly load observations, the proposed DCT-based predictive model combines with the least-squares approach to get the optimum DCT coefficients, which are approximated in the least-squares sense. Then the obtained least-squares-optimum DCT coefficients are employed for forecast modelling to predict the load future movement. Experimental results and analysis show workability of the proposed forecast modelling. It is indicated that the DCT-based least-squares predictive model predicts hourly load movement most fitting with about 12-term DCT coefficients.

• 出版日期2016-11-17
• 单位湖南科技大学