In this paper we approach the problem of forecasting a time-series of electrical load measured on the ACEA power grid, the company managing the electricity distribution in the city of Rome – Italy, with an Echo State Network considering two different leading times of 10 minutes and 1 day. We use a standard approach for predicting the load in the next 10 minutes while, for a forecast horizon of one day, we represent the data with a high dimensional multi-variate time-series, where the number of variables is equal 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 allow us to cast the original prediction problem in k different 1-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 Echo State Network and we compare its prediction accuracy with a standard ARIMA model.

In this paper we approach the problem of forecasting a time-series of electrical load measured on the ACEA power grid, the company managing the electricity distribution in the city of Rome – Italy, with an Echo State Network considering two different leading times of 10 minutes and 1 day. We use a standard approach for predicting the load in the next 10 minutes while, for a forecast horizon of one day, we represent the data with a high dimensional multi-variate time-series, where the number of variables is equal 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 allow us to cast the original prediction problem in k different 1-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 Echo State Network and we compare its prediction accuracy with a standard ARIMA model.

Short-term electric load forecasting using echo state networks and PCA decomposition / Bianchi, FILIPPO MARIA; DE SANTIS, Enrico; Rizzi, Antonello; Sadeghian, Alireza. - In: IEEE ACCESS. - ISSN 2169-3536. - STAMPA. - 3:(2015), pp. 1931-1943. [10.1109/ACCESS.2015.2485943]

Short-term electric load forecasting using echo state networks and PCA decomposition

BIANCHI, FILIPPO MARIA;DE SANTIS, ENRICO;RIZZI, Antonello;
2015

Abstract

In this paper we approach the problem of forecasting a time-series of electrical load measured on the ACEA power grid, the company managing the electricity distribution in the city of Rome – Italy, with an Echo State Network considering two different leading times of 10 minutes and 1 day. We use a standard approach for predicting the load in the next 10 minutes while, for a forecast horizon of one day, we represent the data with a high dimensional multi-variate time-series, where the number of variables is equal 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 allow us to cast the original prediction problem in k different 1-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 Echo State Network and we compare its prediction accuracy with a standard ARIMA model.
2015
In this paper we approach the problem of forecasting a time-series of electrical load measured on the ACEA power grid, the company managing the electricity distribution in the city of Rome – Italy, with an Echo State Network considering two different leading times of 10 minutes and 1 day. We use a standard approach for predicting the load in the next 10 minutes while, for a forecast horizon of one day, we represent the data with a high dimensional multi-variate time-series, where the number of variables is equal 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 allow us to cast the original prediction problem in k different 1-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 Echo State Network and we compare its prediction accuracy with a standard ARIMA model.
Time-series; forecasting; electric load prediction; echo state network; genetic algorithm; PCA; dimensionality reduction; smart grid
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Short-term electric load forecasting using echo state networks and PCA decomposition / Bianchi, FILIPPO MARIA; DE SANTIS, Enrico; Rizzi, Antonello; Sadeghian, Alireza. - In: IEEE ACCESS. - ISSN 2169-3536. - STAMPA. - 3:(2015), pp. 1931-1943. [10.1109/ACCESS.2015.2485943]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/799655
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