STUDY OF ENERGY-SAVING HEATING SCHEDULES OF ELECTRIC WATER BOILERS BASED ON INTERNET OF THINGS

• Main Authors: Ruei-Lin Chien, 簡瑞霖
• Other Authors: Nien-Che Yang
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/56381812464243792008

碩士 === 元智大學 === 電機工程學系 === 104 === The main purpose of this thesis is to develop methodologies for determining the optimal schedules of electric water boilers for end users. In this thesis, discrete particle swarm optimization (DPSO) and decision tree learning based discrete particle swarm optimization (DTLPSO) are used to solve the energy-saving schedules of electric water boilers. The major tasks of this thesis can be divided into two categories: (1) modeling the power consumption behaviors of electric water boilers, and (2) developing the hybrid method based on the discrete particle swarm optimization (DPSO) and decision tree learning interactive dichotomizer-3 (DTL-ID3) optimization. The optimal energy-saving heating schedules of electric water boilers for end users can be obtained based on the power consumption behaviors of electric water boilers, DPSO, DTLDPSO, and historical data of electric water boilers and weather information. After performing the proposed method, the energy consumptions of electric water boilers may be conserved up to 54% without causing any inconvenience. The outcome of this thesis will be applied as a reference for design, planning, operation, control and development of the future scheduling system of electric water boilers, and is of value to promote low carbon energy policy in Taiwan.