A Study of Control Strategies of Water Dispensers for Energy Conservation
| Autor: | YUDHISTIRA CHANDRA BAYU |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Water is crucial thing for human to live. Most important part of water is as a source for human to fulfill the needs of its body by drinking it. However, in Taiwan, water from tap is not safe. Thus many of people in Taiwan use water dispenser to take a drink. Focus on water dispenser, it also consumes a lot of energy by repeating the process such as heating and cooling on its water tank even no one uses it. Having this kind of situation, we take a chance of it by attempting to predict water consumption in water dispenser and utilizing sleep mode feature on water dispenser to save energy from it. Doing prediction with Recurrent Neural Network, we also tried to maintain the service level of water dispenser by putting sleep mode on “right time” since on this mode dispenser does not do any process either heating or cooling Those previous two statements are the main objective of this research. Focusing on water dispenser in university environment surrounded by office and labs, internal data such as water taken from water dispenser and energy usage of water dispenser is collected by attaching sensors on water dispenser that sent data either for each minute or someone takes water from dispenser. Besides internal data, external data is acquired also. We do feature selection to all attributes and Savitzky Golay filtering to water consumption data. Result of feature selection shows: Working and Not Working Hour”, “Temperature”, “Dew Point”, “Clustering Result”, “Consumption Classification”, and “Seasonal Index” are relevant attributes that correlate with water consumption data. For RNN parameters, combination of parameters produces the lowest of error on testing set is: LSTM activation is hard sigmoid, recurrent activation is hard sigmoid, and dense activation is tanH. While on this combination, dataset which utilize filtering water consumption value and related attributes gives the lowest value among other. Based on that parameters combination and dataset. dispenser could save energy about 0.86% of a whole week usage and service level decrease 1.2% |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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