Multi-Task neural network model considering low power output risk for short-term photovoltaic forecasting

The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations. Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation. Therefore, to further study the time correlation for improving the prediction accuracy, an LSTM-FC deep learning algorithm composed of long-term short-term memory (LSTM) and fully connected (FC) layers is proposed. The double-branch input of the model enables it not only to consider the impact of meteorological data on power generation but also to consider time continuity and periodic dependence, thereby improving the prediction accuracy to a certain extent. In this paper, meteorological data, historical continuous data, and historical periodic data are used as experimental data, and these three types of data are combined into different input forms to evaluate and compare LSTM-FC with other baseline models, including support vector machines (SVM), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), feedforward neural network (FFNN), and LSTM. The simulation results show that the accuracy of the models with meteorological data, continuous data, and periodic data as input is higher than that of other input forms, and the accuracy of LSTM-FC is the highest among these models, and its root mean square error (RMSE) is 11.79% higher than that of SVM.

Early detection and timely surgical intervention are crucial in reducing mortality rates associated with clinically significant prostate cancer (csPCa). Currently, clinical diagnostics primarily depend on magnetic resonance imaging (MRI) and nuclear medicine, with the potential diagnostic value of abdominal computed tomography (CT) remaining underexplored. This study aims to evaluate the effectiveness of multi-task deep learning neural networks in identifying early-stage prostate cancer using CT scans. In this study, we enrolled 539 patients from the Department of Radiology (N=461) and Nuclear Medicine (N=78). We utilized a multi-task deep learning network model (MTDL), based on the 3DUnet architecture, to segment and analyze the collected abdominal plain CT images. The predictive performance of this model was compared with a radiomics model and a single-task deep learning model using ResNet18. A diagnostic nomogram was then developed using the multi-task deep learning approach, incorporating prediction results and PSAD, age. The diagnostic performance of the different models was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The 461 patients from the Department of Radiology were divided into training and test sets at a ratio of 6:4, while the patients from the Department of Nuclear Medicine formed the validation set. Our MTDL nomogram demonstrated AUCs of 0.941 (95% confidence interval [CI]: 0.905valceedi 0.912 (95% CI: 0.904valceedi and 0.932 (95% CI: 0.883valceed in the training, test, and validation cohorts, respectively. This study indicates that combining abdominal CT with a multi-task neural network model effectively diagnoses csPCa, offering superior diagnostic performance compared to clinical models. Additionally, the multi-task neural network model outperformed both the single-task neural network model and the radiomics model in diagnostic accuracy. Our study demonstrated that the MTDL nomogram can accurately predict the presence of prostate cancer using abdominal CT scans, offering significant value for the early diagnosis of prostate cancer.

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China’s photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNN-XGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods.

In order to reduce the impact of photovoltaic power output uncertainty on the economic operation of power system, this paper studies the problem of controllable output power of photovoltaic power generation system. In grid connected photovoltaic energy storage system, typical photovoltaic power generation capacity curves obtained by statistical analysis, using particle swarm optimization algorithm to optimize the economic model under different power system output control, when the lowest total cost is the optimal selection of the battery capacity. The battery capacity is determined, the photovoltaic power generation pre measurement, using particle swarm optimization algorithm to optimize the system economic operation model, each time the amount of output of photovoltaic power plant is expected to system debugging, the specific numerical output. Combined with the output of photovoltaic power generation, the output power of the battery is calculated by the analysis and calculation unit, and the economic operation of the grid connected photovoltaic energy storage power generation system is realized through the DC/DC control of the battery charging and discharging.

Challenges of investment and financing for developing photovoltaic power generation in Cameroon, and the countermeasures

Integrated Sustainable Roof Design

The distributed power generation is an energy development way which can most manifest the multiple advantages such as the energy saving, emission reduction, safety and flexible trait. As a kind of distributed power generation technology of renewable energy, photovoltaic power generation has already transited from supplement energy to alternative energy. Although solar photovoltaic power generation industry develops very quickly in China, actual application cases of substation photovoltaic grid-connected power generation system are few. In this paper, the research of photovoltaic grid-connected power generation system is used in Zhuishan substation in Bengbu Anhui province. The system is the first set of substation photovoltaic-connected grid power generation system in Anhui province. It has being operated for three years safety and steady. The whole application of the system is advanced and groundbreaking in the domestic.

With the increasing concern for environmental issues and the rising demand for renewable energy, photovoltaic (PV) power generation has gradually stepped into the public view. This paper provides a comprehensive literature review of key technologies in photovoltaic power generation systems, aiming to summarize and analyze the current state of research, trends and key issues in PV power generation systems. Inverters play a crucial role in PV power generation systems. The role of the inverter is not only to convert electrical energy, but also to ensure that the system can realize the most efficient energy conversion under different light conditions through advanced technologies such as maximum power point tracking. This paper provides a side-by-side comparison of several common tracking techniques from the maximum power point, including Perturb and Observe, Incremental Conductance and Fuzzy Logic Control. At last, this paper discusses future perspectives for photovoltaic power generation technology, including the development of new photovoltaic materials to improve photovoltaic conversion efficiency and cost, as well as the enhancement of the intelligence and integration of photovoltaic power generation systems. These improvements are aimed at promoting photovoltaic power generation technology in the direction of being more efficient, more reliable, more intelligent, and more sustainable.

Solar photovoltaic power generation as an inexhaustible, inexhaustible clean energy has become the focus of future energy development. Along with photovoltaic power generation incorporated into the power grid, in order to make power generation plan reasonably, ensure the stable operation of power system, need to forecast the photovoltaic power output. In this paper solar photovoltaic power generation forecasting methods are analyzed and summarized. According to the application of solar photovoltaic power generation and demand, mainly on photovoltaic power generation system power prediction research method has carried on the comprehensive elaboration, hoping for the researches play an important role in promoting and advancing the development of solar photovoltaic prediction methods.

With the vigorous development of solar photovoltaic power generation, accurate prediction of short-term photovoltaic power generation has become an important issue. However, since the short-term power generation of photovoltaic power generation is affected by many environmental factors and has great uncertainty, the safe operation of the power grid is challenged. Therefore, it is necessary to simulate the power generation in advance through data models and predict the power generation amount. The power grid relies on the valuation and makes corresponding adjustments to maintain the stability of the power grid system.

In order to implement the national energy policy, the rail transit industry actively uses renewable energies such as solar energy to explore ways to cope with energy shortage, ease power shortage and guarantee sustainable development. In this paper, the feasibility, necessity and advantages of applying solar energy to urban rail transit are introduced. Based on the characteristics of urban rail transit, the principle and composition of solar photovoltaic power generation system are analyzed. The application of photovoltaic grid-connected power generation system to urban rail transit vehicle base is proposed Design principles, design of the program and the design of relevant protection measures. The successful cases of the pilot PV grid-connected power generation system in China are summarized. It provides a new idea for the utilization of new energy in the current rail transit design. At the same time, Base roof photovoltaic power generation has done a large-scale promotion and development of accumulated experience for the follow-up project to provide design reference scheme for photovoltaic grid-connected power generation project has an important pioneering significance.

Photovoltaic power generation system is one of the main clean energy power generation systems at present, which plays an important role in daily production and life. However, the photovoltaic power generation system is easily affected by various factors, and the output power will be unstable in the practical application process, which will affect the power generation efficiency. In this paper, a prediction method of distributed energy grid-connected dense data based on federated learning is constructed. This method can not only realize the short-term prediction of distributed photovoltaic power generation data, but also ensure that the data can be encrypted and modeled, thus solving the “digital island” problem. The model evaluation shows that the method in this paper performs well in short-term photovoltaic power generation prediction, and it can predict the short-term power generation of different photovoltaic power stations with high prediction accuracy. This method is of great significance to improve the management and scheduling ability and energy utilization rate of distributed photovoltaic power generation systems.

Solar photovoltaic (PV) power generation is prone to drastic changes due to cloud cover. The power is easily affected within a very short period of time. Thus, the accuracy of grasping cloud distribution is important for PV power forecasting. This study proposes a novel sky image method to obtain the cloud coverage rate used for short-term PV power forecasting. The authors developed an image analysis algorithm from the sky images obtained by an on-site whole sky imager (WSI). To verify the effectiveness of cloud coverage rate as the parameter for PV power forecast, four different combinations of weather features were used to compare the accuracy of short-term PV power forecasting. In addition to the artificial neural network (ANN) model, long short-term memory (LSTM) and the gated recurrent unit (GRU) were also introduced to compare their applicability conditions. After a comprehensive analysis, the coverage rate is the key weather feature, which can improve the accuracy by about 2% compared to the case without coverage feature. It also indicates that the LSTM and GRU models revealed better forecast results under different weather conditions, meaning that the cloud coverage rate proposed in this study has a significant benefit for short-term PV power forecasting.

With the rapid development of photovoltaic power generation technology, photovoltaic power generation system has gradually become an important component of the integrated energy system of marine. High precision short-term photovoltaic power generation forecasting is becoming one of the key technologies in ship energy saving and ship energy efficiency improving. Aiming at the characteristics of marine photovoltaic power generation system, we designed a highprecision power forecasting model (WT+ESN) for marine photovoltaic power generation system with anti-marine environmental interference. In this model, the information mining of the photovoltaic system in marine environment is carried out based on wavelet theory, then the forecasting model basing on echo state network is construct ed. Lastly, three kinds of error metrics are compared with the three traditional models by Matlab, the result shows that the model has high forecasting accuracy and strong robustness to marine environmental factors, which is of great significance to save fuel for ships, improve the energy utilization rate and assist the power dispatching and fuel dispatching of the marine power generation system.

Predicting photovoltaic power generation is important for enhancing the operation and management of photovoltaic power systems, as well as boosting their electricity production efficiency. An ultra-short-term photovoltaic power generation prediction model based on ResNet is proposed to address the current issues of unsatisfactory ground-based cloud image prediction performance and unfavorable marginalization deployment. After analyzing and processing the sky image, the ResNet network is used for feature extraction. Historical photovoltaic power generation data is used as input and fused with the results of feature extraction. Finally, the photovoltaic power generation power is predicted. The result of the experiment showed that the model proposed can effectively extract feature information for predicting ultra-short-term photovoltaic power generation.