Decision Science and Operations Management of Solar Energy System looks beyond developing a solar power plant by also considering the requirements necessary to manage effective power plant operation for the long-term. This book includes data of solar power plants and quantitative techniques of statistical analysis used to inform decision-making for solar energy systems, thus enabling readers to predict future individual solar power system forecasts using different technical and financial parameters. Including data visualization, descriptive statistics, sampling techniques, plant layout, manufacturing economics, inventory management and total quality management of solar energy system, this book covers new insights as well as established fundamentals.
The detailed information in this reference bridges the gap between theory and practice in the operation of solar energy systems for researchers, professionals and students working in the area of solar and renewable energy.
1. Decision Science and Operations Management of Solar Energy Systems - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front Matter - p. Page iv 6. Copyright - p. Page v 7. Contents - p. Page vi 8. List of Illustrations - p. Page vii 9. List of Tables - p. Page viii 10. Chapter 1 : Fundamental and basic principles - p. Page viii 11. Chapter 2 : Data visualization and descriptive statistics of solar energy system - p. Page viii 12. Chapter 3 : Facilities location and plant layout of solar energy system - p. Page viii 13. Chapter 4 : Productivity and manufacturing economics of solar energy system - p. Page viii 14. Chapter 5 : Assessment of solar energy system by probability and sampling distribution - p. Page viii 15. Chapter 6 : Application of regression analysis and forecasting techniques in solar energy system - p. Page viii 16. Chapter 7 : Inventory and total quality management of solar energy system - p. Page viii 17. Chapter 8 : Case study: Solar–wind hybrid renewable energy system - p. Page viii 18. Chapter 9 : Data analysis of solar energy system with Python - p. Page viii 19. Index - p. Page viii
Decision Science and Operations Management of Solar Energy System looks beyond developing a solar power plant by also considering the requirements necessary to manage effective power plant operation for the long-term. This book includes data of solar power plants and quantitative techniques of statistical analysis used to inform decision-making for solar energy systems, thus enabling readers to predict future individual solar power system forecasts using different technical and financial parameters. Including data visualization, descriptive statistics, sampling techniques, plant layout, manufacturing economics, inventory management and total quality management of solar energy system, this book covers new insights as well as established fundamentals.
The detailed information in this reference bridges the gap between theory and practice in the operation of solar energy systems for researchers, professionals and students working in the area of solar and renewable energy.
1. Decision Science and Operations Management of Solar Energy Systems - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front Matter - p. Page iv 6. Copyright - p. Page v 7. Contents - p. Page vi 8. List of Illustrations - p. Page vii 9. List of Tables - p. Page viii 10. Chapter 1 : Fundamental and basic principles - p. Page viii 11. Chapter 2 : Data visualization and descriptive statistics of solar energy system - p. Page viii 12. Chapter 3 : Facilities location and plant layout of solar energy system - p. Page viii 13. Chapter 4 : Productivity and manufacturing economics of solar energy system - p. Page viii 14. Chapter 5 : Assessment of solar energy system by probability and sampling distribution - p. Page viii 15. Chapter 6 : Application of regression analysis and forecasting techniques in solar energy system - p. Page viii 16. Chapter 7 : Inventory and total quality management of solar energy system - p. Page viii 17. Chapter 8 : Case study: Solar–wind hybrid renewable energy system - p. Page viii 18. Chapter 9 : Data analysis of solar energy system with Python - p. Page viii 19. Index - p. Page viii
