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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...
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The power generation from photovoltaic plants depends on varying meteorological conditions. These meteorological conditions such as solar irradiance, temperature, and wind speed, are non-linear and stochastic thus affect estimation of photovoltaic power. Accurate estimation of photovoltaic power is essential for enhancing the functioning of solar p...
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... Traditional forecasting approaches, which primarily leverage historical data and meteorological predictions, often struggle to maintain accuracy under dynamic and complex weather conditions. In recent years, sky imagers have gained prominence as effective tools for monitoring cloud cover, offering valuable insights for solar energy forecasting [4][5][6][7]. However, the application of sky imager data in PV power forecasting still encounters several significant limitations. ...
Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), R2 increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting.
... This procedure is repeated until the loss reaches its minimum value. Since the process of backward propagation of Conv1D-LSTM requires a lengthy explanation, we recommend reading [66][67][68]. The final output of the detector is a set of prediction packets F f uture det based on (3); F s is the sequence of output h of the model: ...
In modern network security setups, Intrusion Detection Systems (IDS) are crucial elements that play a key role in protecting against unauthorized access, malicious actions, and policy breaches. Despite significant progress in IDS technology, two of the most major obstacles remain: how to avoid false alarms due to imbalanced data and accurately forecast the precise type of attacks before they even happen to minimize the damage caused. To deal with two problems in the most optimized way possible, we propose a two-task regression and classification strategy called Hybrid Regression–Classification (HRC), a deep learning-based strategy for developing an intrusion detection system (IDS) that can minimize the false alarm rate and detect and predict potential cyber-attacks before they occur to help the current wireless network in dealing with the attacks more efficiently and precisely. The experimental results show that our HRC strategy accurately predicts the incoming behavior of the IP data traffic in two different datasets. This can help the IDS to detect potential attacks sooner with high accuracy so that they can have enough reaction time to deal with the attack. Furthermore, our proposed strategy can also deal with imbalanced data. Even when the imbalance is large between categories. This will help significantly reduce the false alarm rate of IDS in practice. These strengths combined will benefit the IDS by making it more active in defense and help deal with the intrusion detection problem more effectively.
... Compared to satellite imagery, it boasts superior temporal and spatial resolution, allowing for minute-by-minute calculations and forecasts of photovoltaic power output. For instance, features extracted from ground-based cloud imagery, such as cloud-coverage [12] and cloud-motion trends [13,14], can serve as inputs for predictive models. However, there has been no prior research in the existing research focused on using ground-based cloud imagery for forecasting PV power generation during hazy conditions. ...
Haze constitutes a pivotal meteorological variable with notable implications for photovoltaic power forecasting. The presence of haze is anticipated to lead to a reduction in the output power of photovoltaic plants. Therefore, achieving precise forecasts of photovoltaic power in hazy conditions holds paramount significance. This study introduces a novel approach to forecasting photovoltaic power under haze conditions, leveraging ground-based cloud images. Firstly, the aerosol scattering coefficient is introduced as a pivotal parameter for characterizing photovoltaic power fluctuations influenced by haze. Additionally, other features, such as sky cloud cover, color attributes, light intensity, and texture characteristics, are considered. Subsequently, the Spearman correlation coefficient is applied to calculate the correlation between feature sequences and photovoltaic power. Effective features are then selected as inputs and three models—LSTM, SVM, and XGBoost—are employed for training and performance analysis. After comparing with existing technologies, the predicted results have achieved the best performance. Finally, using actual data, the effectiveness of the aerosol scattering coefficient is confirmed, by exhibiting the highest correlation index, as a pivotal parameter for forecasting photovoltaic output under the influence of haze. The results demonstrate that the aerosol scattering coefficient enhances the forecast accuracy of photovoltaic power in both heavy and light haze conditions by 1.083% and 0.599%, respectively, while exerting minimal influence on clear days. Upon comprehensive evaluation, it is evident that the proposed forecasting method in this study offers substantial advantages for accurately predicting photovoltaic power output in hazy weather scenarios.
... [13] demonstrated PV power forecasting using a hybrid LSTM-based model for one hour ahead. [14] applied the LSTM model to predict short-term PV power output using weather forecast data. [15] employed an LSTMbased deep learning model to forecast PV power generation with high accuracy, incorporating historical PV power data and weather forecast information. ...
As an effort to promote renewable energy-based power generation, one of Malaysia’s initiatives is the net-energy metering (NEM) scheme. One of the shortcomings of residential Photovoltaic (PV) systems under the NEM scheme is that it operates with smart meters only whereby the actual load profiles by the residential consumers remain unknown. Accurate load prediction for NEM consumers is crucial for optimizing energy consumption and effectively managing net metering credits. This study proposes a new model that incorporates an adaptive learning rate and Long Short-Term Memory (LSTM) to predict the solar output power that subsequently predicts the actual load used by the NEM residential consumers. The proposed model is trained and tested using historical time series data of projected PV power and weather conditions, considering the GPS location of the PV system. The outcome of the proposed model is then compared with other state-of-the-art models like ARIMA and regression methods. It is shown that the proposed model outperforms the traditional forecasting models with a Root Mean Square Error (RMSE) value of 0.1942.
... LSTM models, a type of recurrent neural network (RNN), effectively capture temporal dependencies and complex patterns in PV power time series data [15][16][17]. These models have shown significant improvements in accuracy and prediction performance compared to traditional statistical models [18,19]. ...
... For that reason, a sun-blocking mechanism is generally attached to a sky imager [98]. This mechanism is mounted on a 1-axis solar tracker and is similar to the shadow bands used to measure diffuse radiation with pyranometers [99]. Alternative mechanisms that remove less information from sky images exist but are not commercially available [100]. ...
... For the short-term prediction, the given work [10] used a deep neural network-based method. Their approach can handle complex and nonlinear relationships in the data to improve the accuracy of the forecast. ...
The increasing proportion of bifacial photovoltaic modules (Bi-PVM) in new projects makes the operation of photovoltaic system (PVS) more complicated, and it is difficult to accurately predict the power of the PVS. To solve this problem, this paper proposes a new power prediction method for PVS based on Bi-PVM. Firstly, the equal proportion digital twin model of the example project is constructed. The superposition principle is used to analyze the factors affecting the power generation performance of Bi-PVM, and the characteristic project is constructed according to the analysis results. Secondly, the bifacial correction coefficient is introduced to reduce the parameter error caused by Bi-PVM to the prediction model. On this basis, a power prediction machine learning model based on bidirectional gated recurrent unit (Bi-GRU) network is established. Finally, a simulation experiment is carried out on the TensorFlow machine learning platform. With the actual operation data of a PV power station in Jiuquan, China, the simulation analysis is carried out under four weather types, namely, sunny day, rainy day, snowy day and complex and changeable day, respectively, which verified the correctness and excellence of the proposed method.
... For instance, the authors of [19] presented a comprehensive review on the use of Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Extreme Learning Machines (ELM) in smart grids. The work in [20] discussed an interesting application of DL Neural Networks that improves the accuracy of short-term PV power forecasting using sky images. The authors concluded that the cloud coverage rate was a key feature that led to the improvement of the short-term forecasting accuracy by 2%. ...
The integration of Photovoltaic (PV) systems requires the implementation of potential PV power forecasting techniques to deal with the high intermittency of weather parameters. In the PV power prediction process, Genetic Programming (GP) based on the Symbolic Regression (SR) model has a widespread deployment since it provides an effective solution for nonlinear problems. However, during the training process, SR models might miss optimal solutions due to the large search space for the leaf generations. This paper proposes a novel hybrid model that combines SR and Deep Multi-Layer Perceptron (MLP) for one-month-ahead PV power forecasting. A case study analysis using a real Australian weather dataset was conducted, where the employed input features were the solar irradiation and the historical PV power data. The main contribution of the proposed hybrid SR-MLP algorithm are as follows: (1) The training speed was significantly improved by eliminating unimportant inputs during the feature selection process performed by the Extreme Boosting and Elastic Net techniques; (2) The hyperparameters were preserved throughout the training and testing phases; (3) The proposed hybrid model made use of a reduced number of layers and neurons while guaranteeing a high forecasting accuracy; (4) The number of iterations due to the use of SR was reduced. The presented simulation results demonstrate the higher forecasting accuracy (reductions of more than 20% for Root Mean Square Error (RMSE) and 30 % for Mean Absolute Error (MAE) in addition to an improvement in the R2 evaluation metric) and robustness (preventing the SR from converging to local minima with the help of the ANN branch) of the proposed SR-MLP model as compared to individual SR and MLP models.
... In the literature, they are usually divided into three categories [16]; (1) Short-term forecasting is considered to be between a few minutes and a week. Its function is to schedule energy transfer, demand response, and economic dispatch of load [17]. (2) Medium-term forecasting is typically between 1 month and one year and is used to plan the next energy plans. ...
Featured Application
A potential application of this work is to use the PV generation prediction model within an EMS, with the aim of increasing the self-consumption ratio and reducing energy consumption as far as possible.
Abstract
The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable energy production more necessary than ever. Likewise, the prediction of the energy generated in these renewable production plants is key to the implementation of efficient Energy Management Systems (EMS) in buildings. These will aim both to increase the energy efficiency of the building itself, as well as to encourage self-consumption or, where appropriate, collective self-consumption (CSC). This paper presents a comparison between four different models, the former one being an analytical model and the remaining three machine learning (ML) based models. All of them will forecast the photovoltaic (PV) production curve for the next day. In order to validate these models, a case study of a PV system installed on the roof of a university building located in Bidart (France) is proposed. The model that most accurately forecasts the PV production during the period of July 2021 is the support vector regression (SVR), which has a mean R² of 0.934 for July, being 0.97 on sunny days and 0.85 on cloudy ones. This is an improvement of 5.14%, 4.07%, and 4.18% over the nonlinear autoregressive with exogenous inputs (NARX), feedforward neural network (FFNN), and analytical model, respectively.
... Additional studies have employed several extended models based on the ANN and ARIMA models. The adaptive neuro-fuzzy inference system (ANFIS) [16], Infinite impulse response multilayer perceptron (IIR-MLP) [17], locally feedback dynamic fuzzy neural network (LF-DFNN) [18], and deep learning models with several layers such as the convolution neural-network (CNN), recurrent neural network (RNN), long-short term memory neural network (LSTM), and CNN-LSTM and RNN-LSTM hybrid [19][20][21][22][23][24] are examples of extended models based on ANN. These models have demonstrated good predictability. ...
Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R² was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.
