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Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 229
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Book Description
In this project on wind power analysis and forecasting using machine learning with Python, we started by exploring the dataset. We examined the available features and the target variable, which is the active power generated by wind turbines. The dataset likely contained information about various meteorological parameters and the corresponding active power measurements. To begin our analysis, we focused on the regression task of predicting the active power using regression algorithms. We split the dataset into training and testing sets and preprocessed the data by handling missing values and performing feature scaling. The preprocessing step ensured that the data was suitable for training machine learning models. Next, we trained several regression models on the preprocessed data. We utilized algorithms such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. Each model was trained on the training set and evaluated on the testing set using performance metrics like mean squared error (MSE) and R-squared score. After obtaining regression models for active power prediction, we shifted our focus to predicting categorized active power using machine learning models. This involved converting the continuous active power values into discrete categories or classes. We defined categories based on certain thresholds or ranges of active power values. For the categorized active power prediction task, we employed classification algorithms. Similar to the regression task, we split the dataset, preprocessed the data, and trained various classification models. Common classification algorithms used were Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and Light Gradient Boosting models. During the training and evaluation of classification models, we used performance metrics like accuracy, precision, recall, and F1-score to assess the models' predictive capabilities. Additionally, we analyzed the classification reports to gain insights into the models' performance for each category. Throughout the process, we paid attention to feature scaling techniques such as normalization and standardization. These techniques were applied to ensure that the features were on a similar scale and to prevent any bias or dominance of certain features during model training. The results of predicting categorized active power using machine learning models were highly encouraging. The models demonstrated exceptional accuracy and exhibited strong classification performance across all categories. The findings from this analysis have significant implications for wind power forecasting and monitoring systems, allowing for more effective categorization and management of wind power generation based on predicted active power levels. To summarize, the wind power analysis and forecasting session involved dataset exploration, active power regression using regression algorithms, and predicting categorized active power using various machine learning models. The regression task aimed to predict continuous active power values, while the classification task aimed to predict discrete categories of active power. Preprocessing, training, evaluation, and performance analysis were key steps throughout the session. The selected models, algorithms, and performance metrics varied depending on the specific task at hand. Overall, the project provided a comprehensive overview of applying machine learning techniques to analyze and forecast wind power generation.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 229
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Book Description
In this project on wind power analysis and forecasting using machine learning with Python, we started by exploring the dataset. We examined the available features and the target variable, which is the active power generated by wind turbines. The dataset likely contained information about various meteorological parameters and the corresponding active power measurements. To begin our analysis, we focused on the regression task of predicting the active power using regression algorithms. We split the dataset into training and testing sets and preprocessed the data by handling missing values and performing feature scaling. The preprocessing step ensured that the data was suitable for training machine learning models. Next, we trained several regression models on the preprocessed data. We utilized algorithms such as Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. Each model was trained on the training set and evaluated on the testing set using performance metrics like mean squared error (MSE) and R-squared score. After obtaining regression models for active power prediction, we shifted our focus to predicting categorized active power using machine learning models. This involved converting the continuous active power values into discrete categories or classes. We defined categories based on certain thresholds or ranges of active power values. For the categorized active power prediction task, we employed classification algorithms. Similar to the regression task, we split the dataset, preprocessed the data, and trained various classification models. Common classification algorithms used were Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and Light Gradient Boosting models. During the training and evaluation of classification models, we used performance metrics like accuracy, precision, recall, and F1-score to assess the models' predictive capabilities. Additionally, we analyzed the classification reports to gain insights into the models' performance for each category. Throughout the process, we paid attention to feature scaling techniques such as normalization and standardization. These techniques were applied to ensure that the features were on a similar scale and to prevent any bias or dominance of certain features during model training. The results of predicting categorized active power using machine learning models were highly encouraging. The models demonstrated exceptional accuracy and exhibited strong classification performance across all categories. The findings from this analysis have significant implications for wind power forecasting and monitoring systems, allowing for more effective categorization and management of wind power generation based on predicted active power levels. To summarize, the wind power analysis and forecasting session involved dataset exploration, active power regression using regression algorithms, and predicting categorized active power using various machine learning models. The regression task aimed to predict continuous active power values, while the classification task aimed to predict discrete categories of active power. Preprocessing, training, evaluation, and performance analysis were key steps throughout the session. The selected models, algorithms, and performance metrics varied depending on the specific task at hand. Overall, the project provided a comprehensive overview of applying machine learning techniques to analyze and forecast wind power generation.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 612
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Book Description
PROJECT 1: GOLD PRICE ANALYSIS AND FORECASTING USING MACHINE LEARNING WITH PYTHON The challenge of this project is to accurately predict the future adjusted closing price of Gold ETF across a given period of time in the future. The problem is a regression problem, because the output value which is the adjusted closing price in this project is continuous value. Data for this study is collected from November 18th 2011 to January 1st 2019 from various sources. The data has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered. The dataset has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered. To perform forecasting based on regression adjusted closing price of gold, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. The machine learning models used predict gold daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model. PROJECT 2: WIND POWER ANALYSIS AND FORECASTING USING MACHINE LEARNING WITH PYTHON Renewable energy remains one of the most important topics for a sustainable future. Wind, being a perennial source of power, could be utilized to satisfy our power requirements. With the rise of wind farms, wind power forecasting would prove to be quite useful. It contains various weather, turbine and rotor features. Data has been recorded from January 2018 till March 2020. Readings have been recorded at a 10-minute interval. A longterm wind forecasting technique is thus required. The attributes in the dataset are as follows: ActivePower, AmbientTemperature, BearingShaftTemperature, Blade1PitchAngle, Blade2PitchAngle, Blade3PitchAngle, ControlBoxTemperature, GearboxBearingTemperature, GearboxOilTemperature, GeneratorRP, GeneratorWinding1Temperature, GeneratorWinding2Temperature, HubTemperature, MainBoxTemperature, NacellePosition, ReactivePower, RotorRPM, TurbineStatus, WTG, WindDirection, and WindSpeed. To perform forecasting based on regression active power, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm. The machine learning models used predict categorized active power as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: MACHINE LEARNING FOR CONCRETE COMPRESSIVE STRENGTH ANALYSIS AND PREDICTION WITH PYTHON Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. This dataset is in raw form (not scaled). There are 1030 observations, 9 attributes, 8 quantitative input variables, and 1 quantitative output variable in dataset. The attributes in the dataset are as follows: Cement (component 1); Blast Furnace Slag (component 2); Fly Ash (component 3); Water (component 4); Superplasticizer (component 5); Coarse Aggregate; Fine Aggregate (component 7); Age; and Concrete compressive strength. To perform regression on concrete compressive strength, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm. The machine learning models used predict clusters as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: DATA SCIENCE FOR SALES ANALYSIS, FORECASTING, CLUSTERING, AND PREDICTION WITH PYTHON The dataset used in this project is from Walmart which is a renowned retail corporation that operates a chain of hypermarkets. Walmart has provided a data combining of 45 stores including store information and monthly sales. The data is provided on weekly basis. Walmart tries to find the impact of holidays on the sales of store. For which it has included four holidays’ weeks into the dataset which are Christmas, Thanksgiving, Super bowl, Labor Day. In this project, you are going to analyze, forecast weekly sales, perform clustering, and predict the resulting clusters. The dataset covers sales from 2010-02-05 to 2012-11-01. Following are the attributes in the dataset: Store - the store number; Date - the week of sales; Weekly_Sales - sales for the given store; Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week; Temperature - Temperature on the day of sale; Fuel_Price - Cost of fuel in the region; CPI – Prevailing consumer price index; and Unemployment - Prevailing unemployment rate. To perform regression on weekly sales, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm. The machine learning models used predict clusters as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Author: Harsh S. Dhiman
Publisher: Academic Press
ISBN: 0128213671
Category : Science
Languages : en
Pages : 218
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Book Description
Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance. Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation. Features various supervised machine learning based regression models Offers global case studies for turbine wind farm layouts Includes state-of-the-art models and methodologies in wind forecasting
Author: Yu Ding
Publisher: CRC Press
ISBN: 0429956517
Category : Business & Economics
Languages : en
Pages : 400
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Book Description
Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights
Author: André Gensler
Publisher: kassel university press GmbH
ISBN: 3737606366
Category : Weights and measures
Languages : en
Pages : 216
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Book Description
This thesis describes performance measures and ensemble architectures for deterministic and probabilistic forecasts using the application example of wind power forecasting and proposes a novel scheme for the situation-dependent aggregation of forecasting models. For performance measures, error scores for deterministic as well as probabilistic forecasts are compared, and their characteristics are shown in detail. For the evaluation of deterministic forecasts, a categorization by basic error measure and normalization technique is introduced that simplifies the process of choosing an appropriate error measure for certain forecasting tasks. Furthermore, a scheme for the common evaluation of different forms of probabilistic forecasts is proposed. Based on the analysis of the error scores, a novel hierarchical aggregation technique for both deterministic and probabilistic forecasting models is proposed that dynamically weights individual forecasts using multiple weighting factors such as weather situation and lead time dependent weighting. In the experimental evaluation it is shown that the forecasting quality of the proposed technique is able to outperform other state of the art forecasting models and ensembles.
Author: Georges Kariniotakis
Publisher: Woodhead Publishing
ISBN: 0081005059
Category : Technology & Engineering
Languages : en
Pages : 386
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Book Description
Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries Reviews state-of-the-science techniques for renewable energy forecasting Contains chapters on operational applications
Author: Vijendra Singh
Publisher: Springer Nature
ISBN: 9811568766
Category : Technology & Engineering
Languages : en
Pages : 625
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Book Description
This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering (ICMDE 2020), held at SRM University, Sonipat, Delhi-NCR, India. Focusing on cutting-edge technologies and the most dynamic areas of computational intelligence and data engineering, the respective contributions address topics including collective intelligence, intelligent transportation systems, fuzzy systems, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing.
Author: Ajay Kumar Vyas
Publisher: John Wiley & Sons
ISBN: 1119761697
Category : Computers
Languages : en
Pages : 276
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Book Description
ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Author: Ümit Cali
Publisher: kassel university press GmbH
ISBN: 3862190315
Category :
Languages : en
Pages : 174
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Book Description
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 281
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Book Description
In this meteorological data analysis and prediction project using machine learning with Python, we begin by conducting data exploration to understand the dataset's structure and contents. We load the dataset and check for any missing values or anomalies that may require preprocessing. To gain insights into the data, we visualize the distribution of each feature, examining histograms, box plots, and scatter plots. This helps us identify potential outliers and understand the relationships between different variables. After data exploration, we preprocess the dataset, handling missing values through imputation techniques or removing rows with missing data, ensuring the data is ready for machine learning algorithms. Next, we define the problem we want to solve, which is predicting the weather summary based on various meteorological parameters. The weather summary serves as our target variable, while the other features act as input variables. We split the data into training and testing sets to train the machine learning models on one subset and evaluate their performance on unseen data. For the prediction task, we start with simple machine learning models like Logistic Regression or Decision Trees. We fit these models to the training data and assess their accuracy on the test set. To improve model performance, we explore more complex algorithms, such as Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP). We use grid search to tune the hyperparameters of these models and find the best combination that optimizes their performance. During model evaluation, we use metrics such as accuracy, precision, recall, and F1-score to measure how well the models predict the weather summary. To ensure robustness and reliability of the results, we apply k-fold cross-validation, where the dataset is divided into k subsets, and each model is trained and evaluated k times. Throughout the project, we pay attention to potential issues like overfitting or underfitting, striving to strike a balance between model complexity and generalization. Visualizations play a crucial role in understanding the model's behavior and identifying areas for improvement. We create various plots, including learning curves and confusion matrices, to interpret the model's performance. In the prediction phase, we apply the trained models to the test dataset to predict the weather summary for each sample. We compare the predicted values with the actual values to assess the model's performance on unseen data. The entire project is well-documented, ensuring transparency and reproducibility. We record the methodologies, findings, and results to facilitate future reference or sharing with stakeholders. We analyze the predictive capabilities of the models and summarize their strengths and limitations. We discuss potential areas of improvement and future directions to enhance the model's accuracy and robustness. The main objective of this project is to accurately predict weather summaries based on meteorological data, while also gaining valuable insights into the underlying patterns and trends in the data. By leveraging machine learning algorithms, preprocessing techniques, hyperparameter tuning, and thorough evaluation, we aim to build reliable models that can assist in weather forecasting and analysis.
