Distributed photovoltaic(PV)power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data.This paper proposes a lightweight feature transfer learning...Distributed photovoltaic(PV)power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data.This paper proposes a lightweight feature transfer learning(LFTL)method that enables rapid and accurate forecasting of new distributed PVs.Firstly,the raw fluctuating PV data are preprocessed through decomposition to separate low-and high-frequency components.These compo-nents are then multi-scale segmented to capture diverse temporal characteristics.Following feature compression and LSTM temporal modeling,the informative features from the source domain enable lightweight transfer.For the target domain,a channel-independent encoder is designed to prevent negative interactions between het-erogeneous frequencies.The frequency-fused segment-independent decoder equipped with positional embed-dings enables local temporal analysis and reduces error accumulation of multi-step forecasts.LFTL trains with a joint training strategy to avoid negative transfer caused by domain disparity.LFTL consistently outperforms state-of-the-art time-series forecast models while maintaining a relatively low computational overhead based on real-world distributed PV data.展开更多
Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging manageme...Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.展开更多
As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as w...As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.展开更多
This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the iss...This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.展开更多
Accurate Photovoltaic(PV)generation forecasts can reduce power redeploy from the grid,thus increasing the supplier’s profit in the day-ahead electricity market.However,the PV process is affected differently by variou...Accurate Photovoltaic(PV)generation forecasts can reduce power redeploy from the grid,thus increasing the supplier’s profit in the day-ahead electricity market.However,the PV process is affected differently by various factors under different weather conditions,resulting in significantly different energy output curves.In this context,this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism.We propose a Multi-Stream Attention Fusion Network(MSAFN)which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions.The proposed Conditional Decomposition(CD)algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model,aiming to achieve the optimal prediction performance.The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions.Notably,the attention modules adeptly learn patterns under diverse conditions,while simultaneously,the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time.We compare the state-of-the-art decomposition algorithms(VMD,EEMD,MSTL,etc.)and prediction models(BPN,LSTM,XGBoost,transformer,etc.)commonly used in PV prediction.The results show that the MSAFN model is more accurate than the models above,which has a noticeable improvement compared to other recent day-ahead PV predictions on Desert Knowledge Australia Solar Centre(DKASC)dataset.展开更多
Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental impact.Accurate photovoltaic(PV)and wind power(WP)forecasting is crucial...Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental impact.Accurate photovoltaic(PV)and wind power(WP)forecasting is crucial for effective grid management and sustainable energy integration.However,traditional forecasting methods encounter challenges such as data privacy,centralised processing,and data sharing,particularly with dispersed data sources.This review paper thoroughly examines the necessity of forecasting models,methodologies,and data integrity,with a keen eye on the evolving landscape of Federated Learning(FL)in PV and WP forecasting.Commencing with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation,the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security.A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals,followed by in-depth discussions on specific non-Federated Learning approaches for each power source.The paper subsequently introduces FL and its variants,including Horizontal,Vertical,Transfer,Cross-Device,and Cross-Silo FL,highlighting the crucial role of encryption mechanisms and addressing associated challenges.Furthermore,drawing on extensive investigations of numerous pertinent articles,the paper outlines the innovative horizon of FL-based PV and wind power forecasting,offering insights into FL-based methodologies and concluding with observations drawn from this frontier.This review synthesises critical knowledge about PV and WP forecasting,leveraging the emerging paradigm of FL.Ultimately,this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.展开更多
文摘Distributed photovoltaic(PV)power forecasting in newly installed systems faces challenges due to inherent stochastic volatilities and limited historical data.This paper proposes a lightweight feature transfer learning(LFTL)method that enables rapid and accurate forecasting of new distributed PVs.Firstly,the raw fluctuating PV data are preprocessed through decomposition to separate low-and high-frequency components.These compo-nents are then multi-scale segmented to capture diverse temporal characteristics.Following feature compression and LSTM temporal modeling,the informative features from the source domain enable lightweight transfer.For the target domain,a channel-independent encoder is designed to prevent negative interactions between het-erogeneous frequencies.The frequency-fused segment-independent decoder equipped with positional embed-dings enables local temporal analysis and reduces error accumulation of multi-step forecasts.LFTL trains with a joint training strategy to avoid negative transfer caused by domain disparity.LFTL consistently outperforms state-of-the-art time-series forecast models while maintaining a relatively low computational overhead based on real-world distributed PV data.
文摘Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.
基金This project is supported by the National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158).
文摘As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.
文摘This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic(PV)installations.An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation.A day-ahead,hourly mean PV power generation forecasting method based on a combination of genetic algorithm(GA),particle swarm optimization(PSO)and adaptive neuro-fuzzy inference systems(ANFIS)is presented in this study.Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant;and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant.The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing.Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches.The proposed approach outperformed existing artificial neural network(ANN),linear regression(LR),and persistence based forecasting models,validating its effectiveness.
基金supported by the National Natural Science Foundation of China(No.U22A20261)National Key R&D Program of China(No.2023YFB4503903)+6 种基金Gansu Province Science and Technology Major Project—Industrial Project(Nos.22ZD6GA048 and 23ZDGA006)Gansu Province Key Research and Development Plan—Industrial Project(No.22YF7GA004)Fundamental Research Funds for the Central Universities(No.lzujbky-2022-kb12)Open Project of Gansu Provincial Key Laboratory of Intelligent Transportation(No.GJJ-ZH-2024-002)Gansu Provincial Science and Technology Major Special Innovation Consortium Project(No.21ZD3GA002)Science and Technology Plan of Qinghai Province(No.2020-GX164)OPPO Research Fund,Supercomputing Center of Lanzhou University.
文摘Accurate Photovoltaic(PV)generation forecasts can reduce power redeploy from the grid,thus increasing the supplier’s profit in the day-ahead electricity market.However,the PV process is affected differently by various factors under different weather conditions,resulting in significantly different energy output curves.In this context,this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism.We propose a Multi-Stream Attention Fusion Network(MSAFN)which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions.The proposed Conditional Decomposition(CD)algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model,aiming to achieve the optimal prediction performance.The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions.Notably,the attention modules adeptly learn patterns under diverse conditions,while simultaneously,the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time.We compare the state-of-the-art decomposition algorithms(VMD,EEMD,MSTL,etc.)and prediction models(BPN,LSTM,XGBoost,transformer,etc.)commonly used in PV prediction.The results show that the MSAFN model is more accurate than the models above,which has a noticeable improvement compared to other recent day-ahead PV predictions on Desert Knowledge Australia Solar Centre(DKASC)dataset.
基金supported by the Artificial Intelligence,Biomechatronics,and Collaborative Robotics research group at the Top Research Center Mechatronics(TRCM),University of Agder(UIA),Norway.
文摘Renewable energy sources,particularly photovoltaic and wind power,are essential in meeting global energy mands de-while minimising environmental impact.Accurate photovoltaic(PV)and wind power(WP)forecasting is crucial for effective grid management and sustainable energy integration.However,traditional forecasting methods encounter challenges such as data privacy,centralised processing,and data sharing,particularly with dispersed data sources.This review paper thoroughly examines the necessity of forecasting models,methodologies,and data integrity,with a keen eye on the evolving landscape of Federated Learning(FL)in PV and WP forecasting.Commencing with an introduction highlighting the significance of forecasting models in optimising renewable energy resource utilisation,the paper delves into various forecasting techniques and emphasises the critical need for data integrity and security.A comprehensive overview of non-Federated Learning-based PV and WP forecasting is presented based on high-quality journals,followed by in-depth discussions on specific non-Federated Learning approaches for each power source.The paper subsequently introduces FL and its variants,including Horizontal,Vertical,Transfer,Cross-Device,and Cross-Silo FL,highlighting the crucial role of encryption mechanisms and addressing associated challenges.Furthermore,drawing on extensive investigations of numerous pertinent articles,the paper outlines the innovative horizon of FL-based PV and wind power forecasting,offering insights into FL-based methodologies and concluding with observations drawn from this frontier.This review synthesises critical knowledge about PV and WP forecasting,leveraging the emerging paradigm of FL.Ultimately,this work contributes to the advancement of renewable energy integration and the optimisation of power grid management sustainably and securely.
