Random forest solar power forecast based on classification optimization

Solar energy is a type of clean energy and the development of photovoltaic power generation is of significant importance for the achievement of low carbon development in the electric power industry. This paper proposes models and methods for the evaluation of low carbon benefits of grid - connected photovoltaic power generation projects. Firstly, the carbon emissions (or emission reductions) characteristic and economic benefits are analyzed from the following four aspects: revenue of generation capacity, cost of PV, improvement in efficiency of losses, and cost of reserve capacity. Then, corresponding low carbon benefit models and economic benefit models are established. Next, combined with the low carbon and economic characteristics of photovoltaic power generation, the low-carbon comprehensive benefits (LCBs) evaluation model of photovoltaic power generation is created and the concept of carbon-emission payback period (CPP) is put forward. The example based on typical operating data verifies the validity of the models and methods mentioned in this paper. The analysis results show that photovoltaic power generation has a great advantage regarding low-carbon comprehensive benefits, compared to conventional power generation.

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.

The precise forecasting of photovoltaic energy generation holds paramount importance in refining scheduling and ensuring safe operation of extensive photovoltaic power stations. However, the inherent instability and volatility of photovoltaic power generation pose significant challenges to prediction accuracy. To address this, this article conducts a thorough analysis of the seasonal characteristics of photovoltaic power generation and introduces a hybrid prediction model based on the ensemble empirical mode decomposition (EEMD)-improved whale optimization algorithm (IWOA)-bidirectional long short-term memory network (BiLSTM) algorithm. This model leverages multi-seasonal meteorological features to enhance forecasting accuracy. First, EEMD is used to decompose and reconstruct photovoltaic power generation data to eliminate its instability and volatility. Second, three improved strategies are proposed for the position update in different stages of the IWOA, and a multi-seasonal prediction model based on IWOA-optimized Bidirectional LSTM is established. Finally, the operational data of a photovoltaic power station in the northwest region of China are used as a case study to evaluate the prediction performance of the model in detail. The results show that the model's accuracy rate ranges from 97.1% to 98.7%, which can accurately predict photovoltaic power generation and improve the utilization rate of renewable energy.

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.

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

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.

Integrated Sustainable Roof Design

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.

Solar power plays an important role in the transition to a low-carbon energy system, especially in the context of China's latest carbon peaking and neutralization policy. Since the efficiency of Photovoltaic (PV) generation is fundamentally determined by weather conditions, it is characterized by uncertainty, intermittency and constant fluctuation. All these characteristics would pose significant threats to the operational security and stability of the power system. Therefore, it is essential to investigate the optimized operation of distribution network considering the integration of PV generation. For this sake, this paper proposes a novel PV generation modelling framework by applying the concept of “clearness index”, which can effectively capture the output characteristics of PV generation in the short term. Based on this model, this paper establishes an optimization model of distribution network operation, which takes PV plants into consideration. Through a series of case studies tested on the REDS-32 system. It is found that in order to ensure the economic operation of DN, PV power has to be curtailed under certain conditions.

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.

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.

Gray box model and artificial neural network (ANN) model of central air-conditioning cold source system have been established with simulation data to predict system operation energy consumption and compare the prediction accuracy difference of models under variable training data. A hybrid model is proposed to combine gray box model and ANN model based on K-means cluster algorithm. The results show that ANN model has higher prediction accuracy than gray box model when the membership of model input variables to trained input data is greater than 0.4. Otherwise, the gray box model has higher energy consumption prediction accuracy. Compared with gray box model and ANN model, prediction accuracy of hybrid model increases 27.7% and 33.85% on average under different training data.

Due to the influence of solar irradiation, temperature and other environmental factors, the output power of photovoltaic power generation has great randomness and randomness discontinuity. In this paper, a method for analyzing environment data related photovoltaic power generation based on ensembles of decision trees algorithm is studied. Firstly, the characteristics of environmental factors of photovoltaic power generation are analyzed by K-means clustering. And then the corresponding cluster label is assigned. Furthermore, the Radom Forests is combined to build a model. Finally, the method is validated by given data above from a real project. The results show that the proposed method can provide reference for the forecasting of photovoltaic power.

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.

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.