Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065967
Mahsa Khorram, Modar Zheiry, P. Faria, Z. Vale
Nowadays, energy consumption increasing is a big concern for many countries around the world. Disadvantages and consequences of fossil fuels for the environment caused a lot of efforts to invest in renewable energy resources and programs to optimize energy consumption. All types of buildings are the major consumers of electric power. Therefore, buildings can be considered as good options for implementing optimization algorithms, assuming that they are equipped to required infrastructures. Air conditioners are flexible loads that can be directly controlled by optimization programs. This paper presents a particle swarm optimization algorithm to minimize the power consumption of the air conditioners based on the carbon dioxide concentration level. The algorithm considers the thermal comfort of users with defining restrictions. The case study of the paper proposes two scenarios with real monitored data of a building. The result of the paper shows the obtained results of the algorithm and makes the comparison of two scenarios.
{"title":"Air Conditioning Consumption Optimization Based on CO2 Concentration Level","authors":"Mahsa Khorram, Modar Zheiry, P. Faria, Z. Vale","doi":"10.1109/ISAP48318.2019.9065967","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065967","url":null,"abstract":"Nowadays, energy consumption increasing is a big concern for many countries around the world. Disadvantages and consequences of fossil fuels for the environment caused a lot of efforts to invest in renewable energy resources and programs to optimize energy consumption. All types of buildings are the major consumers of electric power. Therefore, buildings can be considered as good options for implementing optimization algorithms, assuming that they are equipped to required infrastructures. Air conditioners are flexible loads that can be directly controlled by optimization programs. This paper presents a particle swarm optimization algorithm to minimize the power consumption of the air conditioners based on the carbon dioxide concentration level. The algorithm considers the thermal comfort of users with defining restrictions. The case study of the paper proposes two scenarios with real monitored data of a building. The result of the paper shows the obtained results of the algorithm and makes the comparison of two scenarios.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"58 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107781","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065936
S. Dutta, Shailesh Verma, P. Sadhu, M. J. B. Reddy, D. Mohanta
Distributed generation (DG) has acquired lots of importance in power industry in recent times. Integration of DGs helps in improving power quality, meeting high demand and maintaining smooth power grid operation. However, incorporation of DGs create complexity in power system protection schemes, such as unintentional islanding. Unintentional islanding has serious consequences on the serviceability and protection of the grid. The proposed methodology is based on voltage measurement directly from a micro-phasor measurement unit ($mutext{PMU}$) and performing Concordia analysis on these signals. Through the technique, it is assessed whether a disturbance occurred at the point of common coupling (PCC) is due to fault or an islanding operation. Various MATLAB simulations are performed using the proposed algorithm to justify its efficiency. Such events are performed on a doubly-fed induction generator (DFIG) based micro-grid.
{"title":"Islanding detection in a distribution system: A pattern assessment based approach using Concordia analysis","authors":"S. Dutta, Shailesh Verma, P. Sadhu, M. J. B. Reddy, D. Mohanta","doi":"10.1109/ISAP48318.2019.9065936","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065936","url":null,"abstract":"Distributed generation (DG) has acquired lots of importance in power industry in recent times. Integration of DGs helps in improving power quality, meeting high demand and maintaining smooth power grid operation. However, incorporation of DGs create complexity in power system protection schemes, such as unintentional islanding. Unintentional islanding has serious consequences on the serviceability and protection of the grid. The proposed methodology is based on voltage measurement directly from a micro-phasor measurement unit ($mutext{PMU}$) and performing Concordia analysis on these signals. Through the technique, it is assessed whether a disturbance occurred at the point of common coupling (PCC) is due to fault or an islanding operation. Various MATLAB simulations are performed using the proposed algorithm to justify its efficiency. Such events are performed on a doubly-fed induction generator (DFIG) based micro-grid.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123024834","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065937
Mehmet Türker Takcı, T. Gözel, M. H. Hocaoğlu
In recent years, estimation algorithms become more popular in terms of forecasting customer behavior or any required data for IT companies. Forecasting results can be used in different purposes such as improving the quality and capacity of production and services, reducing to greenhouse gas emissions, and minimizing the power consumption. The accurate forecasting results are also beneficial for data centers which are the significant participants in the electricity market in terms of consuming huge power demand and have a chance to reduce consumed power, electricity costs by rescheduling their flexible loads for the future period. In this paper, power-consuming devices and variables affecting power consumption are explained. Also, the brief information about artificial neural network and regression analysis methods has been provided. The power consumption of Information Technology devices is forecasted by nonlinear regression analysis and artificial neural network methods. The forecasting results show that artificial neural network method is more successful.
{"title":"Forecasting Power Consumption of IT Devices in a Data Center","authors":"Mehmet Türker Takcı, T. Gözel, M. H. Hocaoğlu","doi":"10.1109/ISAP48318.2019.9065937","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065937","url":null,"abstract":"In recent years, estimation algorithms become more popular in terms of forecasting customer behavior or any required data for IT companies. Forecasting results can be used in different purposes such as improving the quality and capacity of production and services, reducing to greenhouse gas emissions, and minimizing the power consumption. The accurate forecasting results are also beneficial for data centers which are the significant participants in the electricity market in terms of consuming huge power demand and have a chance to reduce consumed power, electricity costs by rescheduling their flexible loads for the future period. In this paper, power-consuming devices and variables affecting power consumption are explained. Also, the brief information about artificial neural network and regression analysis methods has been provided. The power consumption of Information Technology devices is forecasted by nonlinear regression analysis and artificial neural network methods. The forecasting results show that artificial neural network method is more successful.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114284665","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065970
Praneeth M V S S R, C. T. S, P. Yemula
World's transportation system is transforming from conventional gasoline vehicles to electric vehicles. According to Bloomberg electric vehicle outlook 2019 report. During the year 2018, around 2 million electric vehicles are sold. Also, it is expected that approximately 30% of total passenger vehicles will be electric by 2040. This growth in electric vehicles will increase the demand on the grid. For this demand, the utility grid has to go for an increase in the generation or use demand response strategy. With the evolution of demand response and prosumer in the smart grid era, the electric vehicle charging station owner has to schedule his cars for charging. The objective of this paper is to schedule the electric vehicles for charging based on price and solar availability, discharging based on the grid requirement. For this objective, we propose an algorithm called charging station demand response management system (CS-DRMS). Since the charging station owner is participating in demand response and utilizing full solar capability, it is evident that we achieve the profit maximization of the charging station owner. For validation, we discuss the results by simulating the algorithm using MATLAB programming.
{"title":"Scheduling of EV Charging Station for Demand Response Support to Utility","authors":"Praneeth M V S S R, C. T. S, P. Yemula","doi":"10.1109/ISAP48318.2019.9065970","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065970","url":null,"abstract":"World's transportation system is transforming from conventional gasoline vehicles to electric vehicles. According to Bloomberg electric vehicle outlook 2019 report. During the year 2018, around 2 million electric vehicles are sold. Also, it is expected that approximately 30% of total passenger vehicles will be electric by 2040. This growth in electric vehicles will increase the demand on the grid. For this demand, the utility grid has to go for an increase in the generation or use demand response strategy. With the evolution of demand response and prosumer in the smart grid era, the electric vehicle charging station owner has to schedule his cars for charging. The objective of this paper is to schedule the electric vehicles for charging based on price and solar availability, discharging based on the grid requirement. For this objective, we propose an algorithm called charging station demand response management system (CS-DRMS). Since the charging station owner is participating in demand response and utilizing full solar capability, it is evident that we achieve the profit maximization of the charging station owner. For validation, we discuss the results by simulating the algorithm using MATLAB programming.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128250418","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065974
B. Ahmadi, O. Ceylan, A. Ozdemir
Distributed generation (DG), including photovoltaics (PVs), wind turbines (WTs) are becoming vital for Active Distribution Networks (ADN). Therefore, optimal sizing and allocation of these units can improve voltage profiles and reduce active power losses. This study concentrates on optimal allocation and sizing of two kinds of DG units (PVs and WTs) with 1 MW maximum size limits, due to the regulations in Turkey. The Whale optimization algorithm (WOA) and Grey wolf optimization algorithm (GWO) are used as optimization tools to minimize active power losses of 33 and 69 Bus Test Systems. The performance analysis of the methods are performed through simulations and the numerical results are compared in terms of optimal OF values and convergence characteristics.
{"title":"Optimal allocation of multi-type distributed generators for minimization of power losses in distribution systems","authors":"B. Ahmadi, O. Ceylan, A. Ozdemir","doi":"10.1109/ISAP48318.2019.9065974","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065974","url":null,"abstract":"Distributed generation (DG), including photovoltaics (PVs), wind turbines (WTs) are becoming vital for Active Distribution Networks (ADN). Therefore, optimal sizing and allocation of these units can improve voltage profiles and reduce active power losses. This study concentrates on optimal allocation and sizing of two kinds of DG units (PVs and WTs) with 1 MW maximum size limits, due to the regulations in Turkey. The Whale optimization algorithm (WOA) and Grey wolf optimization algorithm (GWO) are used as optimization tools to minimize active power losses of 33 and 69 Bus Test Systems. The performance analysis of the methods are performed through simulations and the numerical results are compared in terms of optimal OF values and convergence characteristics.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114106316","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065975
Anand Kumar, Debayan Sarkar, P. Sadhu
Unique advantages of induction heating (IH) technology in terms of efficiency and performance has increased its endorsement in industrial along with domestic applications. Owing to this, the usage of IH technology for domestic cooking is being increased and the increasing application of this technology is leading to power quality issue in terms of THD, low power factor problem and electromagnetic interference (EMI) effect. In order to mitigate these issues, in this paper a domestic IH system has been designed using boost power factor correction (BPFC) converter which acts as a front end converter. The BPFC converter corrects the input power factor (PF) as well as regulates the DC link voltage. This DC link voltage is fed to the full bridge series resonant inverter (FB-SRI) which generates high frequency AC which is the need of domestic IH system. A 2.2 kW domestic IH system has been designed and validated through the Power simulation (PSIM) software.
{"title":"Boost Power Factor Correction Converter fed Domestic Induction Heating System","authors":"Anand Kumar, Debayan Sarkar, P. Sadhu","doi":"10.1109/ISAP48318.2019.9065975","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065975","url":null,"abstract":"Unique advantages of induction heating (IH) technology in terms of efficiency and performance has increased its endorsement in industrial along with domestic applications. Owing to this, the usage of IH technology for domestic cooking is being increased and the increasing application of this technology is leading to power quality issue in terms of THD, low power factor problem and electromagnetic interference (EMI) effect. In order to mitigate these issues, in this paper a domestic IH system has been designed using boost power factor correction (BPFC) converter which acts as a front end converter. The BPFC converter corrects the input power factor (PF) as well as regulates the DC link voltage. This DC link voltage is fed to the full bridge series resonant inverter (FB-SRI) which generates high frequency AC which is the need of domestic IH system. A 2.2 kW domestic IH system has been designed and validated through the Power simulation (PSIM) software.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114882230","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065969
C. Silva, P. Faria, Z. Vale
Giving the small resources more information about the transactions in the market will have a great influence on the balance and increase the uncertainty. Business models that are prepared to deal with small consumers and/or with small Distributed Generation units need to emerge to deal with this problem. The authors present a methodology able to minimize the operation costs for the Aggregator of these small resources but also find a fair remuneration according to their participation in the management of the local grid. The methodology could be explored by two approaches depending on time horizon: planning or operation. In the present paper, the two will be compared showing the viability of the path selected by the authors for the real-time approach - assign a remuneration group to a consumer considering the actual participation and the rules provided by a classification method.
{"title":"Real-Time Approach for Demand Response Tariffs Definition Using Decision Trees","authors":"C. Silva, P. Faria, Z. Vale","doi":"10.1109/ISAP48318.2019.9065969","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065969","url":null,"abstract":"Giving the small resources more information about the transactions in the market will have a great influence on the balance and increase the uncertainty. Business models that are prepared to deal with small consumers and/or with small Distributed Generation units need to emerge to deal with this problem. The authors present a methodology able to minimize the operation costs for the Aggregator of these small resources but also find a fair remuneration according to their participation in the management of the local grid. The methodology could be explored by two approaches depending on time horizon: planning or operation. In the present paper, the two will be compared showing the viability of the path selected by the authors for the real-time approach - assign a remuneration group to a consumer considering the actual participation and the rules provided by a classification method.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126203854","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065930
Yingshan Tao, Fei Zhao, Haoliang Yuan, Chun Sing Lai, Zhao Xu, Wing W. Y. Ng, Rongwei Li, Xuecong Li, L. Lai
The application of artificial neural network to load forecasting can overcome the problem of dynamic load change, and its ability to adapt to nonlinear relationships makes the prediction result satisfactory. This paper firstly reviews and introduces the concepts and basic principles of load prediction, discusses various methods for load forecasting, and then selects artificial neural network to establish a predictive model. In this paper, the European electric load is predicted with a BP neural network. From the prediction results, it is feasible to use BP neural network for load forecasting, and its accuracy can meet the needs of real-life engineering work. However, BP neural networks have the problem of slow convergence and easily falling into local minimum points. Therefore, this paper also uses three other neural networks for load forecasting, which are Radial Basis Network (RBF), Elman Network, and Long-Short Term Memory Network (LSTM). In the experiment, the four neural networks achieved expected prediction results, and the LSTM network had the best prediction effect. Scientific discussions are offered.
{"title":"Revisit Neural Network based Load Forecasting","authors":"Yingshan Tao, Fei Zhao, Haoliang Yuan, Chun Sing Lai, Zhao Xu, Wing W. Y. Ng, Rongwei Li, Xuecong Li, L. Lai","doi":"10.1109/ISAP48318.2019.9065930","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065930","url":null,"abstract":"The application of artificial neural network to load forecasting can overcome the problem of dynamic load change, and its ability to adapt to nonlinear relationships makes the prediction result satisfactory. This paper firstly reviews and introduces the concepts and basic principles of load prediction, discusses various methods for load forecasting, and then selects artificial neural network to establish a predictive model. In this paper, the European electric load is predicted with a BP neural network. From the prediction results, it is feasible to use BP neural network for load forecasting, and its accuracy can meet the needs of real-life engineering work. However, BP neural networks have the problem of slow convergence and easily falling into local minimum points. Therefore, this paper also uses three other neural networks for load forecasting, which are Radial Basis Network (RBF), Elman Network, and Long-Short Term Memory Network (LSTM). In the experiment, the four neural networks achieved expected prediction results, and the LSTM network had the best prediction effect. Scientific discussions are offered.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519637","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065949
R. Manojkumar, H. V M, Chandan Kumar, S. Ganguly
Uncertainty and variation of power generation through photovoltaic (PV) sources are major challenges for their integration with the distribution grid. Voltage rise and voltage drop issues limit the increase in PV penetration and loading level, respectively. It is important to maintain voltage levels within specified limits of grid code for providing long life, more efficiency, and good performance of consumer equipment while ensuring that the PV power generation is not curtailed. In this paper, a voltage control method for the smart transformer (ST) is proposed to improve voltage profile in the distribution network. Voltage control capability for ST is added through the method of switching among three setpoints based on the voltage. The proposed method is compared with the conventional method of switching between two setpoints based on current. The proposed method provides better voltage profile in the distribution network as compared to conventional method. Performance indicators are developed to understand the impact of voltage control methods on the system voltage profile. Proposed voltage control method is tested on a CIGRE low voltage residential distribution network.
{"title":"Voltage Control Using Smart Transformer for Increasing Photovoltaic Penetration in a Distribution Grid","authors":"R. Manojkumar, H. V M, Chandan Kumar, S. Ganguly","doi":"10.1109/ISAP48318.2019.9065949","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065949","url":null,"abstract":"Uncertainty and variation of power generation through photovoltaic (PV) sources are major challenges for their integration with the distribution grid. Voltage rise and voltage drop issues limit the increase in PV penetration and loading level, respectively. It is important to maintain voltage levels within specified limits of grid code for providing long life, more efficiency, and good performance of consumer equipment while ensuring that the PV power generation is not curtailed. In this paper, a voltage control method for the smart transformer (ST) is proposed to improve voltage profile in the distribution network. Voltage control capability for ST is added through the method of switching among three setpoints based on the voltage. The proposed method is compared with the conventional method of switching between two setpoints based on current. The proposed method provides better voltage profile in the distribution network as compared to conventional method. Performance indicators are developed to understand the impact of voltage control methods on the system voltage profile. Proposed voltage control method is tested on a CIGRE low voltage residential distribution network.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126188665","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065983
Omid Abrishambaf, P. Faria, Z. Vale
Agriculture sector is backbone of each country. Nowadays the energy efficiency in this sector is at a very low level, which shows the necessity of more investments in this regard. By appearance of smart grid technologies, some new concepts were also appeared in the agriculture sector, such as smart farm, and smart agriculture. This paper provides an energy management system for an agriculture field equipped with renewable energy resources and a river turbine. A decision tree is developed in this paper to schedule and optimize the use of energy resources for reducing the electricity costs. Decision tree method enables the system to obtain optimal scheduling of energy resources in offline mode, without using any external server/machine or internet access. A case study validates the performance of developed decision tree, and the errors and accuracy of all gained results are discussed.
{"title":"Energy Resource Scheduling in an Agriculture System Using a Decision Tree Approach","authors":"Omid Abrishambaf, P. Faria, Z. Vale","doi":"10.1109/ISAP48318.2019.9065983","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065983","url":null,"abstract":"Agriculture sector is backbone of each country. Nowadays the energy efficiency in this sector is at a very low level, which shows the necessity of more investments in this regard. By appearance of smart grid technologies, some new concepts were also appeared in the agriculture sector, such as smart farm, and smart agriculture. This paper provides an energy management system for an agriculture field equipped with renewable energy resources and a river turbine. A decision tree is developed in this paper to schedule and optimize the use of energy resources for reducing the electricity costs. Decision tree method enables the system to obtain optimal scheduling of energy resources in offline mode, without using any external server/machine or internet access. A case study validates the performance of developed decision tree, and the errors and accuracy of all gained results are discussed.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128464568","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}
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