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Can I read Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study on EtoBox?
Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study by Xiao Chen; Benyi Cao; Somayeh Pouramini is a Engineering article available to read on EtoBox.
What is Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study about?
A large amount of energy consumed globally is done by buildings, also, buildings are responsible for a great portion of greenhouse gas emissions. With progress in smart sensors and devices, a new generation of smarter and more context-aware building controllers can be developed. Consequently, zone-level surrogate artificial neural networks are used herein, where indoor temperature, occupancy, and weather data are the inputs. A new metaheuristic optimization algorithm, called Chaotic Satin Bowerbird Optimization Algorithm (CSBOA) is employed for the minimization of energy consumption. 24-hour schedules of the heating setpoint of each zone are created for an office building located in Edinburgh, Scotland. Two modes of optimization including day-ahead and model predictive control are applied for each hour. The consumption of energy decreased by 26% during a test week in Feb in comparison to the base case approach of heating. By definition of a time-of-use tariff, the cost of energy consumption is decreased by around 28%.
Who reads Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Xiao Chen; Benyi Cao; Somayeh Pouramini
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)