Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven...Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.展开更多
In terms of planning aspect,nuclear power plant(NPP)development needs analyses,consideration,and right decision making due to multi criteria involved.This study prioritizes the best site development of Indonesian NPPs...In terms of planning aspect,nuclear power plant(NPP)development needs analyses,consideration,and right decision making due to multi criteria involved.This study prioritizes the best site development of Indonesian NPPs in terms of 21 social,economic,and technical perspectives which comprise transmission network,oper-ating cost,economic impact,geology,geotechnic,seismology,population density,environment,cooling water,meteorology,hydrology,proximity to hazardous facilities,topography,land use,proximity to wetland,evacu-ation route,security,transportation network,legal consideration,impact of tourism,land ownership,historical places,and public acceptance,all identified to be considerations for the best sites.Two Fuzzy algorithms(Chang’s Extent Analysis and Buckley’s Fuzzy AHP)were used to determine the criteria priorities as well as NPP site feasibility of two locations in Indonesia.The results found that geology,geotechnic,and seismology(SA1);security(SO1),population density(SA2),environment(SA3),and cooling water(SA4)had the highest priorities among the 21 criteria.Based on the 5 top priority criteria,West Kalimantan and East Kalimantan provinces serve as the best candidates for the NPP sites.Such an innovative and novel multi criteria Fuzzy AHP–based decision making(MCDM)approach has been proven to become a useful reference to select NPP sites in Indonesia.展开更多
基金support from the Ministry of Science and Technology of Taiwan(Contract Nos.113-2221-E-011-130-MY2 and 113-2218-E-011-002)the support from Intelligent Manufactur-ing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan.
文摘Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.
文摘In terms of planning aspect,nuclear power plant(NPP)development needs analyses,consideration,and right decision making due to multi criteria involved.This study prioritizes the best site development of Indonesian NPPs in terms of 21 social,economic,and technical perspectives which comprise transmission network,oper-ating cost,economic impact,geology,geotechnic,seismology,population density,environment,cooling water,meteorology,hydrology,proximity to hazardous facilities,topography,land use,proximity to wetland,evacu-ation route,security,transportation network,legal consideration,impact of tourism,land ownership,historical places,and public acceptance,all identified to be considerations for the best sites.Two Fuzzy algorithms(Chang’s Extent Analysis and Buckley’s Fuzzy AHP)were used to determine the criteria priorities as well as NPP site feasibility of two locations in Indonesia.The results found that geology,geotechnic,and seismology(SA1);security(SO1),population density(SA2),environment(SA3),and cooling water(SA4)had the highest priorities among the 21 criteria.Based on the 5 top priority criteria,West Kalimantan and East Kalimantan provinces serve as the best candidates for the NPP sites.Such an innovative and novel multi criteria Fuzzy AHP–based decision making(MCDM)approach has been proven to become a useful reference to select NPP sites in Indonesia.
