2018 IEEE Green Technologies Conference (GreenTech)最新文献_第3页

Energy Efficient Control Methods of HVAC Systems for Smart Campus 智能校园暖通空调系统节能控制方法

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00032

Caleb Petrie, Smit Gupta, Vittal S. Rao, B. Nutter

{"title":"Energy Efficient Control Methods of HVAC Systems for Smart Campus","authors":"Caleb Petrie, Smit Gupta, Vittal S. Rao, B. Nutter","doi":"10.1109/GREENTECH.2018.00032","DOIUrl":"https://doi.org/10.1109/GREENTECH.2018.00032","url":null,"abstract":"The incorporation of advanced sensor data acquisition techniques, concepts of cyber physical systems, and the Internet of Things (IoT) devices into infrastructures and building energy management systems is enabling the smart city deployments. The main emphasis of this paper is to design and implementation of energy efficient controls into the heating, ventilation, and air conditioning (HVAC) systems of the commercial buildings. These systems are responsible for providing acceptable indoor air quality and thermal comfort to the occupants. By tuning and implementing advanced control systems into the existing HVAC systems, energy consumption can be reduced by 20-30%. The traditional control techniques are compared with two advanced control techniques: Pattern Recognition Adaptive Controller (PRAC) and Model Predictive Control (MPC). The salient features of the advanced control techniques are presented. By utilizing the existing hardware and software systems, the proposed control techniques are implemented on one of the academic buildings of the Texas Tech University campus. The preliminary results obtained in the minimization of simultaneous heating and cooling and energy savings of the HVAC systems are presented in the paper.","PeriodicalId":387970,"journal":{"name":"2018 IEEE Green Technologies Conference (GreenTech)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114214807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Household Load Forecasting Based on a Pre-Processing Non-Intrusive Load Monitoring Techniques 基于预处理非侵入式负荷监测技术的家庭负荷预测

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00028

Ahmed F. Ebrahim, O. Mohammed

{"title":"Household Load Forecasting Based on a Pre-Processing Non-Intrusive Load Monitoring Techniques","authors":"Ahmed F. Ebrahim, O. Mohammed","doi":"10.1109/GREENTECH.2018.00028","DOIUrl":"https://doi.org/10.1109/GREENTECH.2018.00028","url":null,"abstract":"The new vision for moving the power system to a smart grid enables a variety of smart applications at different power system infrastructure's levels. Households represent a massive section of the grid infrastructure which could not be considered as a smart grid without smart households integrated into it. Short Term Load Forecasting (STLF) is the essential tool needed in the management and control techniques required for households to be smart. STLF at this level of the grid is very challenging due to the high percentage of uncertainty in the load demand, influenced by customer behavior, which is too stochastic to predict. In this paper, a new approach for STLF of household load demand is employed based on artificial neural network (ANN) and a pre-processing stage of a Non-Intrusive Load Monitoring (NILM) techniques. The NILM techniques extract the individual load pattern from the available historical aggregated load demand. These new features increase the training data window for the ANN forecaster and achieve a significant enhancement for its prediction performance. By comparing the new approach with the state of the art techniques in household load forecasting, the proposed method outperforms feed-forward artificial neural network (FFANN) regarding RMSE. Two techniques of NILM were used to emphasize the correlation between the NILM disaggregation accuracy performance and the load forecasting enhancement performance.","PeriodicalId":387970,"journal":{"name":"2018 IEEE Green Technologies Conference (GreenTech)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121118822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Break-Even Analysis of Battery Energy Storage in Buildings Considering Time-of-Use Rates 考虑时间利用率的建筑物电池储能盈亏平衡分析

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00026

Mucun Sun, Chih-Lun Chang, Jie Zhang, Ali Mehmani, P. Culligan

{"title":"Break-Even Analysis of Battery Energy Storage in Buildings Considering Time-of-Use Rates","authors":"Mucun Sun, Chih-Lun Chang, Jie Zhang, Ali Mehmani, P. Culligan","doi":"10.1109/GREENTECH.2018.00026","DOIUrl":"https://doi.org/10.1109/GREENTECH.2018.00026","url":null,"abstract":"As energy consumption in residential and commercial buildings continues to grow, demand-side management (DSM) for energy systems becomes crucial, because DSM can shift energy use from peak to off-peak hours. In order to realize peak load shifting, energy storage systems (ESSs) can be integrated into buildings to store energy during off-peak hours and discharge energy in peak hours. However, installing a large number of ESSs in individual buildings can complicate DSM and increase the overall capital cost. In this paper, a cost-effective DSM strategy is proposed to address this energy management challenge. The break-even cost of battery storage in a building is explored through a process of two-step optimization in conjunction with different tariff structures. A number of scenarios are performed to conduct cost analyses of the storage-based building energy system under different time-of-use rate structures. The performance of the DSM strategy in the battery break-even cost, is explored using a particle swarm optimization algorithm based on the size of energy storage and priced-based constraints of the energy system. Results of a case study show that the proposed approach can reduce the peak-to-average ratio of the total energy demand to the total energy costs. In addition, as the percentage reductions in yearly maximum energy peaks increase, the optimal battery cost becomes progressively more economical to building owners.","PeriodicalId":387970,"journal":{"name":"2018 IEEE Green Technologies Conference (GreenTech)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134270865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Influence of Production and Investment Tax Credit on Renewable Energy Growth and Power Grid 生产投资税收抵免对可再生能源增长和电网的影响

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00035

Chesta Dwivedi

引用次数: 2

Model Predictive Control for Building Energy Reduction and Temperature Regulation 建筑节能与温度调节模型预测控制

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00027

Tian Zhang, M. Wan, B. Ng, Shiyu Yang

{"title":"Model Predictive Control for Building Energy Reduction and Temperature Regulation","authors":"Tian Zhang, M. Wan, B. Ng, Shiyu Yang","doi":"10.1109/GREENTECH.2018.00027","DOIUrl":"https://doi.org/10.1109/GREENTECH.2018.00027","url":null,"abstract":"Building climate control mechanisms account for more than 50% of the overall residential and commercial sector energy usage. Other than undertaking complementary green building design procedure to cut down the operational cost, optimal control of air-conditioning and mechanism ventilation (ACMV) systems in existing buildings is mutually important. While current building manage systems (BMS) usually operates with proportional-integral controllers to maintain constant component set points, there is no supervisory optimization for overall system operation under various conditions. In this paper, we propose a model predictive control (MPC)-based optimal temperature controller suitable for on-line optimization for smart buildings equipped with sensors. The proposed MPC controller integrates building thermodynamics, occupancy data, weather forecast data, as well as ACMV component models for minimizing energy consumption as well as stabilizing building temperature. To ensure feasibility during real-time operation, the above mentioned optimization is further decoupled into two sub-optimizations, dealing with system thermodynamics and component power consumption characteristics separately. In the simulation studies, the proposed MPC controller is able to achieve as much as 18.2% energy saving with different temperature regulation settings.","PeriodicalId":387970,"journal":{"name":"2018 IEEE Green Technologies Conference (GreenTech)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126153127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Green Electronic Based Chipless Humidity Sensor for IoT Applications 用于物联网应用的绿色电子无芯片湿度传感器

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00039

Sumra Zeb, Ayesha Habib, Y. Amin, H. Tenhunen, J. Loo

引用次数: 13

Automatic Identification of Use of Public Transportation from Mobile Sensor Data 基于移动传感器数据的公共交通使用自动识别

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 2018-04-01 DOI: 10.1109/GREENTECH.2018.00042

Mohammadreza Hajy Heydary, Pritesh Pimpale, A. Panangadan

{"title":"Automatic Identification of Use of Public Transportation from Mobile Sensor Data","authors":"Mohammadreza Hajy Heydary, Pritesh Pimpale, A. Panangadan","doi":"10.1109/GREENTECH.2018.00042","DOIUrl":"https://doi.org/10.1109/GREENTECH.2018.00042","url":null,"abstract":"Automatic analysis of a user's activity using data from smartphones has become commonplace. Current methods can distinguish modes of transportation such as standing still, walking, running, and traveling in a motor vehicle. However, there is not yet a way to determine automatically if a person is using public transportation or in a private vehicle. Developing this capability will enable novel ways of promoting public transportation use for sustainability. For instance, this information can be used to provide route-specific product/shopping recommendations/coupons. This work presents a novel means of identifying use of public transportation (bus) using sensor data typically collected using smartphones. The method extracts orientation-invariant features from segments of sensor measurements and then uses a subset of the data to train a random forest classifier. The trained classifier is able to identify which segments of accelerometer and gyroscope data represent instances of public transportation use. This method is evaluated using real-world data collected in the Los Angeles County and Orange County areas in southern California. Our results indicate that the method is able to distinguish the mode of transportation of the user in most cases with an f-score of approximately 96%.","PeriodicalId":387970,"journal":{"name":"2018 IEEE Green Technologies Conference (GreenTech)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134364244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Challenges of IoT Based Smart-Government Development 基于物联网的智慧政府发展的挑战

2018 IEEE Green Technologies Conference (GreenTech) Pub Date : 1900-01-01 DOI: 10.1109/greentech.2018.00036

A. Alenezi, Zainab Almeraj, Paul Manuel

引用次数: 24