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[IEEE 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Miramar Beach, FL, USA (2019.8.19-2019.8.21)] 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Invited Talk: "High Resolution Space/Time Imaging of Shockwaves Generated by Remote Laser Plasmas Produced by Light Filaments"
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： PCA,dimensionality reduction,electric load prediction,smart grid,genetic algorithm,forecasting,echo state network,Time-series
更新于2025-09-19 17:13:59
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[IEEE 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Shenzhen, China (2019.11.25-2019.11.27)] 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Research on a Smart LED Lighting Based on Improved Flyback Driver
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： genetic algorithm,forecasting,PCA,echo state network,Time-series,smart grid,electric load prediction,dimensionality reduction
更新于2025-09-19 17:13:59