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Flowchart for insolation forecast using a multi-stage neural network (Adapted from Kemoku et al., 1999)

Flowchart for insolation forecast using a multi-stage neural network (Adapted from Kemoku et al., 1999)

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Article
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Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of...

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... authors propose a multi-stage NN method for forecasting the insolation of the next day. Figure 8 shows the block diagram of the multi-stage NN used for forecasting the insolation, proposed by Kemoku et al. (1999). Meteorological data at Omaezaki, Japan in 1988-1993 are used as input data, and the insolations in 1994 are forecast. ...

Citations

... Next, we have carried out a literature survey to understand the contemporary work done in this area. Fuzzy logic, AI models, and genetic algorithms are used to predict and model solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems in [1]. Ensemble of deep ConvNets is proposed for multistep solar forecasting without additional time series models like RNN or LSTM and exogenous variables in [2] with 22.5% RMSE. ...
... The vector autoregressive model of order one is denoted as VAR (1). Similarly, in a VAR (2) model, the lag two values for all variables are added to the right sides of the equations. ...
Preprint
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Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar irradiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accomplished on the dataset obtained from the 1MW solar power plant of PDEU (our university) that has generated data set with 13 columns as daily entries from 2012 to 2020. There are 12 structured columns and one unstructured column with manual text entries about different maintenance activities, problems observed, and weather conditions daily. The unstructured column is used to create a new feature column vector using Hash Map, flag words, and stop words. The final dataset comprises five important feature vector columns based on correlation and causality analysis. Further, the random forest regression is used to compute the impact of maintenance activities on the total energy output. The causality and correlation analysis has shown that the five feature vectors are interdependent time series variables. Next, Vector Autoregression (VAR) is chosen for simultaneous forecasting of total power generation for 3, 5, 7, 10, 12, and 30 days ahead using the VAR model. The results have shown that the root means square percentage error (RMSPE) in total power generation forecasting is less than 10% for different days. This research has proven that the spikes in total power generation forecasting can be traced and tracked better using daily maintenance activities, observed problems, and weather conditions.
... Next, we have carried out a literature survey to understand the contemporary work done in this area. Fuzzy logic, AI models, and genetic algorithms are used to predict and model solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems in [1]. Ensemble of deep ConvNets is proposed for multistep solar forecasting without additional time series models like RNN or LSTM and exogenous variables in [2] with 22.5% RMSE. ...
... The vector autoregressive model of order one is denoted as VAR (1). Similarly, in a VAR (2) model, the lag two values for all variables are added to the right sides of the equations. ...
Preprint
Full-text available
Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accomplished on the datasets obtained from the 1MW solar power plant of PDEU (our university) that has generated data set with 13 columns as daily entries from 2012 to 2020. There are 12 structured columns and one unstructured column with manual text entries about different maintenance activities, problems observed, and weather conditions daily. The unstructured column is used to create a new feature column vector using Hash Map, flag words, and stop words. The final dataset comprises five important feature vector columns based on correlation and causality analysis.
... AI has power and capability for faster and more accurate predictions than any other traditional methods. So, it has more potential in the environmental and renewable energy applications (Belu, 2012). Artificial intelligence (AI) has characteristics of intelligence like humans which is demonstrated by machines. ...
... The AI techniques have the potential for making highquality or superior, faster, and other extra or further practical predictions than any of the other methods. Also, data that are collected from the renewable energy processes being naturally rumbustious so they are a good candidate to be handled with AI systems (Belu, 2012). AI is used to provide innovative ways of solving issues and will allow designers to get a faster opinion on the effect of a change in a design (Kalogirou, 2010). ...
Article
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To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced technology. The most readily available source of energy is solar energy but solar energy has nonlinear nature due to the random nature of climate conditions. So, one way to solve is solar radiation prediction and solar energy prediction using more accurate techniques. Also, energy business and power system control units require more accuracy along with very short to large duration prediction in advance. So, to complete the requirement many prediction techniques are used and among them, Artificial Neural Network (ANN) and Fuzzy are more accurate and reliable techniques. In this paper basically, a literature study for solar radiation and energy prediction using ANN and Fuzzy logic techniques has been carried out. Many studies are reviewed and then selected some most accurate, reliable, and relevant studies for further study. ANN models with different algorithms such as feed-forward back-propagation-based ANN, Multi-layer feed-forward-based ANN model, Linear regression with ANN model, GNN-based model are reviewed in the study. ANN models with different input parameters combinations and the different number of neurons were also reviewed. Fuzzy logic-based and Adaptive Neuro-Fuzzy interface (ANFIS)-based different models have been reviewed and observed that the ANFIS technique performs better. From the study, it has been noted that ANN and Fuzzy logic employed models are most effective for estimation than any other empirical models. It is found that solar radiation and energy prediction models are dependent on input parameters more. At last, highlighted some possible research opportunities and areas for better efficiency of the results.
... During the design and construction of various objects (residential houses, high-rise office complexes, various fences, etc.), certain parts and facades of buildings are covered with photovoltaic modules. This method allows for saving space, and efficiently using the free space of various constructions of buildings and other objects for electric energy production [3][4][5][6]. ...
Article
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An electronic monitoring system was developed to monitor and analyze operating and environmental parameters of solar power plants. The electronic monitoring system consisted of two stages: the first stage was designed to receive data from temperature, illumination, voltage, electric current, and power sensors and modules; the second stage is for data collection, analysis, and display. Microcontrollers PIC18F25K22 (for data collection) and PIC24FV32KA302 (for data analysis and display) were used to implement the electronic monitoring system. RF module EBTYE OEM/ODM E32-433T20DC SX1278 433 MHz UART was used to transmit data to the central server. For the microcontrollers of both stages, original control program algorithms were developed, according to which program codes were written using the C programming language. Data were collected using the MySQL database. The virtual interface and application for displaying instant data were created using programming language Delphi. Data monitoring systems in the market have a number of shortcomings due to the difficulty of ensuring a centralized data management process. In addition, sensors used in most systems require a Wi-Fi Internet connection to transmit information, which is difficult to secure in remote solar parks. The aim of this work is to show that information transmission from individually deployed sensors to the central server can be ensured using the principle of RF communication, and data collection and analysis in a centralized way without additional costs for Wi-Fi networks in remote areas.
... Artificial Intelligence (AI) methods have achieved competitive prediction performance due to their success in extracting the complex underlying structure of the solar data [6]. AI methods, especially deep learning (DL) algorithms, can be implemented without feature engineering and are less sensitive to missing data [7]. ML techniques, including Linear Regression (LR), K-Nearest Neighbor (KNN), Decision Trees (DT), Gaussian Process Regression (GPR), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR), and Multi-layer Perceptron Regression (MLPR), are used for many forecasting tasks. ...
Preprint
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The use of solar photovoltaics (PV) energy provides additional resources to the electric power grid. The downside of this integration is that the solar power supply is unreliable and highly dependent on the weather condition. The predictability and stability of forecasting are critical for the full utilization of solar power. This study reviews and evaluates various machine learning-based models for solar PV power generation forecasting using a public dataset. Furthermore, The root mean squared error (RMSE), mean squared error (MSE), and mean average error (MAE) metrics are used to evaluate the results. Linear Regression, Gaussian Process Regression, K-Nearest Neighbor, Decision Trees, Gradient Boosting Regression Trees, Multi-layer Perceptron, and Support Vector Regression algorithms are assessed. Their responses against false data injection attacks are also investigated. The Multi-layer Perceptron Regression method shows robust prediction on both regular and noise injected datasets over other methods.
... Artificial Intelligence (AI) methods have achieved competitive prediction performance due to their success in extracting the complex underlying structure of the solar data [6]. AI methods, especially deep learning (DL) algorithms, can be implemented without feature engineering and are less sensitive to missing data [7]. ML techniques, including Linear Regression (LR), K-Nearest Neighbor (KNN), Decision Trees (DT), Gaussian Process Regression (GPR), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR), and Multi-layer Perceptron Regression (MLPR), are used for many forecasting tasks. ...
Conference Paper
Full-text available
The use of solar photovoltaics (PV) energy provides additional resources to the electric power grid. The downside of this integration is that the solar power supply is unreliable and highly dependent on the weather condition. The predictability and stability of forecasting are critical for the full utilization of solar power. This study reviews and evaluates various machine learning-based models for solar PV power generation forecasting using a public dataset. Furthermore, The root mean squared error (RMSE), mean squared error (MSE), and mean average error (MAE) metrics are used to evaluate the results. Linear Regression, Gaussian Process Regression, K-Nearest Neighbor, Decision Trees, Gradient Boosting Regression Trees, Multi-layer Perceptron, and Support Vector Regression algorithms are assessed. Their responses against false data injection attacks are also investigated. The Multi-layer Perceptron Regression method shows robust prediction on both regular and noise injected datasets over other methods. Index Terms-Solar PV energy generation forecasting, noise impact, and forecasting.
... The review's main objective is to examine state of the art using artificial intelligence (AI) techniques and tools in power management, maintenance, and control of renewable energy and specifically to the solar power systems. The last review related to AIs [35]. This study's findings shall allow researchers to innovate the current state of technologies and possibly use the standard and successful techniques in building AI-powered renewable energy systems. ...
Conference Paper
Full-text available
This paper's main objective is to examine the state of the art of artificial intelligence (AI) techniques and tools in power management, maintenance, and control of renewable energy systems (RES) and specifically to the solar power systems. The findings would allow researchers to innovate the current state of technologies and possibly use the standard and successful techniques in building AI-powered renewable energy systems, specifically for solar energy. Various peer-reviewed journal articles were examined to determine the condition and advancement of the AI techniques in the field of RES, specifically in solar power systems. Different theoretical and experimental AI techniques often used and reliable techniques determined were the Artificial Neural Network (ANN), Backpropagation Neural Network (BPNN), Adaptive Neuro�Fuzzy Inference System (ANFIS), and Genetic Algorithm (GA). These techniques are widely used in different types of solar predictions based on the findings of this review. However, ANN stood out as the best of these techniques. ANN's specific advantages over its competition include short computing time, higher accuracy, and generalization capabilities over other modeling techniques. This would translate to cost efficiency over other modeling techniques.
... Depending on the problem to be solved, ANNs are good in some applications and lack in some others [73]. ANNs are good in tasks with incomplete data and for complex problems where humans usually give solutions on an intuition basis [74]. ANNs lack in applications that require high accuracy and precision for example problems involving arithmetic and logic. ...
Thesis
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Solar energy powered systems are increasingly being implemented in different areas due to the advances in solar energy technologies. Some of the major areas for solar energy applications include solar water heating, solar electric power generation, and solar water pumping. Solar water pumping has become the most adopted solar energy technology in the last decade. It has been considered as an attractive way to provide water in remote areas. A major advantage of using solar water pumps is that they are naturally matched with solar irradiation since usually water demand is high in summer when solar irradiation has its maximum values. However, solar energy powered systems are weather dependent. In most cases, a solar energy source has to be combined with another energy source to form a hybrid system to overcome the demerits of using solar alone. This thesis provides the detailed design, modelling and analysis of an Artificial Intelligence (AI) based solar/diesel hybrid water pumping system. This research aims to develop an optimization model that uses AI techniques to maximize the solar energy output and manage the energy flow within the solar/diesel hybrid water pumping. Thus, the proposed system is composed of solar photovoltaic modules, battery bank, Variable Speed Diesel Generator (VSDG), Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) controllers and an Energy Management Controller (EMC). The EMC, which is based on Fuzzy Logic (FL), is responsible for managing the flow of energy throughout the hybrid system to ensure an undisturbed power supply to the water pump. The PV array, battery bank, VSDG are all sized to power a 5Hp DC water pump and the ANFIS based MPPT controllers are proposed for improving the efficiency of PV modules. The modelling of the system components is performed in the MATLAB/Simulink environment. For evaluation of the proposed system, several case scenarios were considered and simulated in the MATLAB/Simulink environment. The simulation results revealed the effectiveness of the proposed ANFIS based MPPT controllers since the controllers were able to extract maximum available power from PV modules for both steady-state and varying weather conditions. The proposed EMC demonstrated the successful management and control of the energy flow within the hybrid system with less dependency on the VSDG. The EMC was also able to regulate the charging and discharging of the battery bank.
... In order to find answers to questions such as from what source, where and to what extent the power plant where the investment will be made, studies are carried out on artificial intelligence models. For example, the use of artificial intelligence can be observed in determining the control parameters of a photovoltaic power plant [48] or calculating the wind potential of a region [49]. ...
Article
Full-text available
It is an inevitable fact that the applications of machine learning and artificial intelligence technologies in agricultural biotechnology approaches, whose applications are increasing rapidly in almost every field, will have an important place in determining the future fate of agriculture. Applications in which artificial intelligence is adapted to biotechnological processes such as breeding, in vitro culture studies, germplasm preservation, disease-free plant production, genetic transformation, and other genetic analyzes are becoming increasingly common. In the current study is highlighted the potential benefits between artificial intelligence and agricultural technologies. As with many plant species, viral diseases have negative effects on fruit yield, life span, and quality of olive varieties, which are important in economy. Elimination of viruses from the plant with traditional methods is quite laborious, takes a lot of time, and often fails to produce successful results. However, new protocols have been developed to eliminate persistent pathogens. These protocols include techniques such as heat application (thermotherapy), chemical therapy (chemotherapy), tissue culture methods (meristem culture). When these techniques are applied alone or together, it may be possible to obtain anti-virus plants. Artificial intelligence technology will make it possible to benefit from the method to be used in the most efficient way by revealing which of these biotechnological methods can be used in the most effective and optimal conditions, and the possible advantages and disadvantages as a result of comparing with others.
... Other uses involved the financial market, where AI has been used in the dynamic pricing and fraud detection [9]. In the energy domain, AI is used to reduce the electricity [10] and solar modelling [11]. In the agriculture AI has been used in the detection of fruit ripening [12]. ...
Preprint
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Artificial Intelligence has been a growth catalyst to our society and is cosidered across all idustries as a fundamental technology. However, its development has been limited to the signal processing domain that relies on the generated and collected data from other sensors. In recent research, concepts of Digital Artificial Intelligence and Physicial Artifical Intelligence have emerged and this can be considered a big step in the theoretical development of Artifical Intelligence. In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Physicial Artifical Intelligence and Distributed Physicial Artifical Intelligence. The paper will also examine the trend and governance of Physicial Artifical Intelligence.