Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutio...
Contexts in source publication
Context 1
... algorithm was independently executed 30 times. Table 4 shows the parameter settings of the comparison algorithm, and Table 5 shows the experimental results. Table 4. Setting parameters of GWO, WOA, SO, and MISO. ...Context 2
... 4. Setting parameters of GWO, WOA, SO, and MISO. Table 5 indicates that MISO exhibits remarkable performance advantages for unimodal test functions. When solving functions f 1 (x), f 2 (x), and f 3 (x), the MISO algorithm achieves the theoretical optimum, which is far superior to SO and other compared algorithms. ...Similar publications
Given a chain of $HW$ cubes where each cube is marked "turn $90^\circ$" or "go straight", when can it fold into a $1 \times H \times W$ rectangular box? We prove several variants of this (still) open problem NP-hard: (1) allowing some cubes to be wildcard (can turn or go straight); (2) allowing a larger box with empty spaces (simplifying a proof fr...
Citations
... 2) The LSTM model was utilized for day-ahead forecasting. Wang et al. [98] 2024. 6 ISO + CNN + BiLSTM 1) K-means clustering was conducted to categorize weather patterns into three distinct classes. ...
... Wang et al. [98] constructed a MISO-CNN-BiLSTM prediction model and introduced Improved Snake Optimization (MISO) to optimize the hyperparameters of BiLSTM. They set up multiple control groups for comparison and found that the proposed model had higher prediction accuracy under various weather conditions. ...
... Figure 18: Comparison of models for cloudy weather. Reprinted from Reference [98] Herrera et al. [97] used BOA to optimally adjust the hyperparameters of the LSTM model and then used the optimized model for day-ahead PV power prediction. They compared the results with those of GRU and MLP models. ...
With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. Therefore, this paper reviews the PV power prediction methods from five aspects: influencing factors, evaluation indexes, prediction status, difficulties and future trends. Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification of model features, statistical methods, artificial intelligence methods, and combined methods of prediction. Finally, the development trend of PV power generation prediction technology and possible future research directions are envisioned.
... RNNs perform well in time-series data, and they are especially suitable for the analysis of dynamic and time-dependent data, such as vibration signals of machines and sensor data for anomaly detection [26]. For example, in complex time-series anomaly detection, models based on RNN variants such as long short-term memory networks (LSTMs) have achieved good results by capturing temporal dependencies between sequences, making them promising for industrial surveillance [27,28]. ...
Effectively identifying and preventing anomalies in the melt process significantly enhances production efficiency and product quality in industrial manufacturing. Consequently, this paper proposes a study on a melt anomaly identification system for pelletizers using autoencoder technology. It discusses the challenges of detecting anomalies in the melt extrusion process of polyester pelletizers, focusing on the limitations of manual monitoring and traditional image detection methods. This paper proposes a system based on autoencoders that demonstrates effectiveness in detecting and differentiating various melt anomaly states through deep learning. By randomly altering the brightness and rotation angle of images in each training round, the training samples are augmented, thereby enhancing the system’s robustness against changes in environmental light intensity. Experimental results indicate that the system proposed has good melt anomaly detection efficiency and generalization performance and has effectively differentiated degrees of melt anomalies. This study emphasizes the potential of autoencoders in industrial applications and suggests directions for future research.
... With the advances in deep learning, many deep learning-based models have been made in the field of renewable energy generation forecasting [9,10]. As an extension of artificial neural networks (ANNs), deep learning constructs multilayer neural networks to simulate the learning processes of the human brain, offering exceptional nonlinear modeling capabilities and adaptability. ...
As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.
... It is noteworthy to understand that the OLS model investigates relationships between multiple variables to estimate or predict the value of one variable based on others that are correlated [52]. The BP model, characterized by a three-layer neural network (input, hidden, and output), connects neurons in the hidden layer through specific weights adjusted by the Adam optimizer using the back-propagation algorithm to optimize parameters until minimal error is achieved [53]. The ANN model is proficient in capturing intricate, nonlinear relationships across multiple inputs and outputs, effectively optimizing, predicting, forecasting, and controlling diverse systems [54]. ...
This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO2 emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with Thailand’s goals for sustainability development, as outlined in the green economy objectives. The research employs a quantitative research approach, utilizing Linear Structural Relationships based on a Latent Growth Model (LISREL-LGM model) which is a valuable tool for efficient country management towards predefined green economy objectives by 2033. The research findings reveal continuous significant growth in the past economic sector (1990–2023), leading to subsequent growth in the social sector. Simultaneously, this growth has had a continuous detrimental impact on the environment, primarily attributed to the economic growth in the industrial building sector. Consequently, the research indicates that maintaining current policies would result in CO2 emissions from energy consumption in the industrial building sector exceeding the carrying capacity. Specifically, the growth rate (2033/2024) would increase by 28.59%, resulting in a surpassing emission of 70.73 Mt CO2 Eq. (2024–2033), exceeding the designated carrying capacity of 60.5 Mt CO2 Eq. (2024–2033). Therefore, the research proposes strategies for country management to achieve sustainability, suggesting the implementation of new scenario policies in the industrial building sector. This course of action would lead to a reduction in CO2 emissions (2024–2033) from energy consumption in the industrial building sector to 58.27 Mt CO2 Eq., demonstrating a decreasing growth rate below the carrying capacity. This underscores the efficacy and appropriateness of the LISREL-LGM model employed in this research for guiding decision making towards green economy objectives in the future.
