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Yuyang Gao
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

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Journal article
Published: 09 January 2020 in Applied Energy
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Reliable forecast of electricity can encourage accessible and responsible information for scholars, policymakers, end-consumers and managers of the electricity market. Numerous electricity forecasting methods have been achieved commendably, the performance of which varies depending on numerical characteristics and operational conditions. In this study, the composite forecasting concept is introduced and implemented to show the potential of forecasting performance. This modeling concept is a remarkable ability to identify and measure any seasonal relationship that exists in electricity demand data. Moreover, it is available as a toolbox in many of the programming operation research. In the module of nonlinear time series decomposition, the noise disturbance is initially considered before extracting the seasonal variation to support the condition that the linear and stationary time series should be used for the seasonality identifying method. Also, we further provide a new insight of prediction intervals estimation to better reflect the uncertainties of the underlying challenging power system plan and operation. The results show that the proposed model can generate promising forecasts compared to the other combination schemata and it can be useful for both policy-makers and public agencies to guarantee the security and regulation of the power system.

ACS Style

Ping Jiang; Ranran Li; Ningning Liu; Yuyang Gao. A novel composite electricity demand forecasting framework by data processing and optimized support vector machine. Applied Energy 2020, 260, 114243 .

AMA Style

Ping Jiang, Ranran Li, Ningning Liu, Yuyang Gao. A novel composite electricity demand forecasting framework by data processing and optimized support vector machine. Applied Energy. 2020; 260 ():114243.

Chicago/Turabian Style

Ping Jiang; Ranran Li; Ningning Liu; Yuyang Gao. 2020. "A novel composite electricity demand forecasting framework by data processing and optimized support vector machine." Applied Energy 260, no. : 114243.

Journal article
Published: 14 June 2018 in Energies
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Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.

ACS Style

Jiyang Wang; Yuyang Gao; Xuejun Chen. A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting. Energies 2018, 11, 1561 .

AMA Style

Jiyang Wang, Yuyang Gao, Xuejun Chen. A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting. Energies. 2018; 11 (6):1561.

Chicago/Turabian Style

Jiyang Wang; Yuyang Gao; Xuejun Chen. 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting." Energies 11, no. 6: 1561.