Renewable Energy for Buildings : Technology, Control, and Operational Techniques

The book covers practical applications and experimental results of integrating renewable energy technologies, energy storage facilities, and intelligent control and operation techniques into building energy systems. It introduces practical approaches to improving the energy systems of buildings in order to reduce energy consumption and cost. Renewable Energy for Buildings is suitable for retrofit engineers, energy engineers, and professionals, as well as researchers and developers in electrical engineering, architectural engineering, and mechanical engineering. Moreover, it can be used by undergraduate and graduate students to become familiar with the most recent developments in building energy systems. ​Examines the most recent developments in building energy systems; Looks at practical applications and theoretical aspects; Includes case studies. Contents About the Editors Introduction and Literature Review of Building Components with Passive Thermal Energy Storage Systems Introduction Sensible Heat Storage Storage Mediums with High Heat Capacity Stone Earth Brick Concrete Solar Walls Trombe Wall Water Wall Latent Heat Storage Incorporation in Wall Construction Direct Incorporation Immersion Impregnation Encapsulation in Building Walls Macroencapsulation Microencapsulation Form-Stable Composite and Shape-Stabilized PCM Conclusion References The Impact of Large Deployment of Distributed Solar Photovoltaic at the Urban Scale on the Building Performance and the Correl... Introduction Solar PV Generation Potential for Buildings Buildings ́ Energy and Electrical Demand The Impact of PV Deployment at the Urban Scale Solar Design The Correlation Between PV Energy Production and the Electricity Demand Performance-Based Design Process Conventional Design Process Parametric Design Process Generative Design Optimization Electrification of End-Use Services and Future Impact on the Electric System Conclusion References Deep Learning-Assisted Solar Radiation Forecasting for Photovoltaic Power Generation Management in Buildings Introduction Deep Learning-Based Methodologies Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Solar Radiation Forecasting Results Conclusion References A Survey on Home Energy Management Systems with Viewpoints of Concepts, Configurations, and Infrastructures Introduction HEMS Overview Concept Capabilities and Features of HEMS Functionalities of HEMS HEMS Infrastructures Communication and Networking System Smart Meter Center of Systems that Manage Home Energy Consumption Home Gadgets Intelligent HEMS for Smart Homes Applications of Intelligent Control Techniques Exchange Protocols and Role of the Smart Sensors Load and Energy Scheduling Techniques Programmable Home Appliance Consumption Planning Methods for HEMS Motivation-Based Demand Response Programs Methods Household Appliances Modeling and Control Schematic Conclusions References Optimal Energy Management of Residential Buildings to Supply Controllable and Uncontrollable Loads Under Uncertainty Introduction Modeling of Residential Building Mathematical Modeling Objective Function Power Balance Network Constraint CHP Photovoltaic Gas Boiler Battery Energy Storage Electric and Absorption Chillers Electric Heat Pump Controllable Heating and Cooling Loads ON/OFF Electric Loads Mathematical Model of Robust Optimization Robust Uncertainty Management Model of the Residential Building Simulation and Numerical Results Input Data Simulation Results Conclusion References Occupancy Data Sensing, Collection, and Modeling for Residential Buildings Introduction The Impact of Occupants on Energy Consumption Occupancy Data Sensing, Collection, and Management for Buildings Occupancy Data Types and Categorization Sensor-Based Occupancy Data Collection Survey-Based Occupancy Data Collection Occupancy Data for Energy Management Occupancy Behaviors Impacting Energy Performance of Buildings Methods of Occupancy Modeling Influencing Occupant Behavior Conclusions References Index Buildings account for about 40% of total global energy demand from which the building space HVAC contribute about 50% to it [1]. Hence, reducing energy consumption at the site level by improving building energy efficiency has gained a lot of attention over the past years. Also, TES systems in buildings facilitate achieving a grid-interactive efficient building (GEB) since they bring more flexibility for the building load control [2]. If TES systems become commercially available and operationally verified, GEB provides greater grid reliability, thus promoting the integration of renewables in a smart grid. That being said, researchers have tried to develop more reliable TES control systems that can perform well under uncertainties