mahtab kaffash - Academia.edu (original) (raw)

Papers by mahtab kaffash

Research paper thumbnail of Comparison of Statistical-Based and Data-Driven-Based Scenario Generation of PV Power for Stochastic Day-Ahead Battery Scheduling

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2020

The day-ahead PV power generation scenarios, which represent the possible output of PV, have a si... more The day-ahead PV power generation scenarios, which represent the possible output of PV, have a significant impact on the scheduling of the available flexibility in a smart building. In this paper, a scenario generation approach for the day-ahead production of a single PV system is presented. The proposed method is important in the context of single buildings where the self-consumption has to be optimized. LASSO, which is a data-driven method, is used in order to select relevant quantiles to capture CDF. Moreover, a statistical-based scenario generation is applied in order to compare the performance of the proposed method. The generated scenarios for day-ahead PV generation are used in a stochastic problem to minimize the expected operational cost of a building and manage the flexibility, which is battery in this case study. Finally, the proposed method has been applied to a real PV installation on the rooftop of EnergyVille-1, a research institute. The simulation results demonstrate...

Research paper thumbnail of Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation

Energies, 2021

Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, ha... more Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, have become popular. For a more efficient use of MESs, the optimal operation of these systems needs to be considered. This paper focuses on the day-ahead optimal schedule of an MES, including a combined heat and electricity (CHP) unit, a gas boiler, a PV system, and energy storage devices. Starting from a day-ahead PV point forecast, a non-parametric probabilistic forecast method is proposed to build the predicted interval and represent the uncertainty of PV generation. Afterwards, the MES is modeled as mixed-integer linear programming (MILP), and the scheduling problem is solved by interval optimization. To demonstrate the effectiveness of the proposed method, a case study is performed on a real industrial MES. The simulation results show that, by using only historical PV measurement data, the point forecaster reaches a normalized root-mean square error (NRMSE) of 14.24%, and the calibrat...

Research paper thumbnail of Battery Scheduling in a Residential Multi-Carrier Energy System Using Reinforcement Learning

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2018

Motivated by the recent developments in machine learning and artificial intelligence, this work c... more Motivated by the recent developments in machine learning and artificial intelligence, this work contributes to the application of reinforcement learning in Multi-Carrier Energy Systems (MCESs) to provide flexibility at the residential level. The work addresses the problem of providing flexibility through the operation of a storage device, and flexibility of supply by considering several infrastructures to meet the residential thermal and electrical demand in a MCES with a photovoltaic (PV) installation. The problem of providing flexibility using a battery is formulated as a sequential decision making problem under uncertainty where, at every time step, the uncertainty is due to the lack of knowledge about future electricity demand and weather dependent PV production. This paper proposes to address this problem using fitted Q-iteration, a batch Reinforcement Learning (RL) algorithm. The proposed method is tested using data from a typical Belgian residential household. Simulation resu...

Research paper thumbnail of A Method To Model And Forecast Seasonal Load Duration Curve

In power system studies, seasonal load duration curve (LDC) plays an important role in medium ter... more In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method. Keywords—artificial neural network (ANN); forecasting; load duration curve (LDC); modeling; seasonal load duration curve

Research paper thumbnail of Ensemble Machine Learning Forecaster for Day Ahead PV System Generation

In this paper, the application of machine learning methods to predict the day ahead photovoltaic ... more In this paper, the application of machine learning methods to predict the day ahead photovoltaic power generation in hourly intervals from the previous days, without using any exogenous data, have been studied. In order to select the relevant features, a random forest feature selection is used. This paper proposes a forecasting approach based on ensembles of artificial neural networks and support vector regression. The focus of this paper is on a single installed photovoltaic system, and in order to evaluate the performance of the proposed approaches, the measured data related to the photovoltaic installation on the roof of EnergyVille-1 is used. The results show that proposed approach can improve the accuracy of forecasting.

Research paper thumbnail of No. E-14-AAA-0000 A Method To Model And Forecast Seasonal Load Duration Curve

Abstract—In power system studies, seasonal load duration curve (LDC) plays an important role in m... more Abstract—In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method. Keywords—artificial neural network (ANN); forecasting; load duration curve (LDC); modeling; seasonal load duration curve

Research paper thumbnail of Data‐driven forecasting of local PV generation for stochastic PV ‐battery system management

International Journal of Energy Research

Research paper thumbnail of Utilization of financial contracts by wind power plants to benefit promotion and risk reduction

2016 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG), 2016

Research paper thumbnail of A combinational maximum power point tracking algorithm in photovoltaic systems under partial shading conditions

2016 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG), 2016

Research paper thumbnail of A Method To Model And Forecast Seasonal Load Duration Curve

In power system studies, seasonal load duration curve (LDC) plays an important role in medium ter... more In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method.

Research paper thumbnail of A Method To Model And Forecast Seasonal Load Duration Curve

In power system studies, seasonal load duration curve (LDC) plays an important role in medium ter... more In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method.

Research paper thumbnail of Comparison of Statistical-Based and Data-Driven-Based Scenario Generation of PV Power for Stochastic Day-Ahead Battery Scheduling

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2020

The day-ahead PV power generation scenarios, which represent the possible output of PV, have a si... more The day-ahead PV power generation scenarios, which represent the possible output of PV, have a significant impact on the scheduling of the available flexibility in a smart building. In this paper, a scenario generation approach for the day-ahead production of a single PV system is presented. The proposed method is important in the context of single buildings where the self-consumption has to be optimized. LASSO, which is a data-driven method, is used in order to select relevant quantiles to capture CDF. Moreover, a statistical-based scenario generation is applied in order to compare the performance of the proposed method. The generated scenarios for day-ahead PV generation are used in a stochastic problem to minimize the expected operational cost of a building and manage the flexibility, which is battery in this case study. Finally, the proposed method has been applied to a real PV installation on the rooftop of EnergyVille-1, a research institute. The simulation results demonstrate...

Research paper thumbnail of Interval Optimization to Schedule a Multi-Energy System with Data-Driven PV Uncertainty Representation

Energies, 2021

Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, ha... more Recently, multi-energy systems (MESs), whereby different energy carriers are coupled together, have become popular. For a more efficient use of MESs, the optimal operation of these systems needs to be considered. This paper focuses on the day-ahead optimal schedule of an MES, including a combined heat and electricity (CHP) unit, a gas boiler, a PV system, and energy storage devices. Starting from a day-ahead PV point forecast, a non-parametric probabilistic forecast method is proposed to build the predicted interval and represent the uncertainty of PV generation. Afterwards, the MES is modeled as mixed-integer linear programming (MILP), and the scheduling problem is solved by interval optimization. To demonstrate the effectiveness of the proposed method, a case study is performed on a real industrial MES. The simulation results show that, by using only historical PV measurement data, the point forecaster reaches a normalized root-mean square error (NRMSE) of 14.24%, and the calibrat...

Research paper thumbnail of Battery Scheduling in a Residential Multi-Carrier Energy System Using Reinforcement Learning

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2018

Motivated by the recent developments in machine learning and artificial intelligence, this work c... more Motivated by the recent developments in machine learning and artificial intelligence, this work contributes to the application of reinforcement learning in Multi-Carrier Energy Systems (MCESs) to provide flexibility at the residential level. The work addresses the problem of providing flexibility through the operation of a storage device, and flexibility of supply by considering several infrastructures to meet the residential thermal and electrical demand in a MCES with a photovoltaic (PV) installation. The problem of providing flexibility using a battery is formulated as a sequential decision making problem under uncertainty where, at every time step, the uncertainty is due to the lack of knowledge about future electricity demand and weather dependent PV production. This paper proposes to address this problem using fitted Q-iteration, a batch Reinforcement Learning (RL) algorithm. The proposed method is tested using data from a typical Belgian residential household. Simulation resu...

Research paper thumbnail of A Method To Model And Forecast Seasonal Load Duration Curve

In power system studies, seasonal load duration curve (LDC) plays an important role in medium ter... more In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method. Keywords—artificial neural network (ANN); forecasting; load duration curve (LDC); modeling; seasonal load duration curve

Research paper thumbnail of Ensemble Machine Learning Forecaster for Day Ahead PV System Generation

In this paper, the application of machine learning methods to predict the day ahead photovoltaic ... more In this paper, the application of machine learning methods to predict the day ahead photovoltaic power generation in hourly intervals from the previous days, without using any exogenous data, have been studied. In order to select the relevant features, a random forest feature selection is used. This paper proposes a forecasting approach based on ensembles of artificial neural networks and support vector regression. The focus of this paper is on a single installed photovoltaic system, and in order to evaluate the performance of the proposed approaches, the measured data related to the photovoltaic installation on the roof of EnergyVille-1 is used. The results show that proposed approach can improve the accuracy of forecasting.

Research paper thumbnail of No. E-14-AAA-0000 A Method To Model And Forecast Seasonal Load Duration Curve

Abstract—In power system studies, seasonal load duration curve (LDC) plays an important role in m... more Abstract—In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method. Keywords—artificial neural network (ANN); forecasting; load duration curve (LDC); modeling; seasonal load duration curve

Research paper thumbnail of Data‐driven forecasting of local PV generation for stochastic PV ‐battery system management

International Journal of Energy Research

Research paper thumbnail of Utilization of financial contracts by wind power plants to benefit promotion and risk reduction

2016 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG), 2016

Research paper thumbnail of A combinational maximum power point tracking algorithm in photovoltaic systems under partial shading conditions

2016 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG), 2016

Research paper thumbnail of A Method To Model And Forecast Seasonal Load Duration Curve

In power system studies, seasonal load duration curve (LDC) plays an important role in medium ter... more In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method.

Research paper thumbnail of A Method To Model And Forecast Seasonal Load Duration Curve

In power system studies, seasonal load duration curve (LDC) plays an important role in medium ter... more In power system studies, seasonal load duration curve (LDC) plays an important role in medium term horizon power system planning, reliability and energy markets studies, and economic analysis of electric power systems. Therefore, finding a simple and accurate model to forecast LDC is beneficial to network operators as well as market participants. This paper proposes a new framework to forecast seasonal LDC. As there are few contributions regarding forecasting curve time series, we redefine the problem of forecasting LDCs into a vector forecasting problem. In fact, we divide LDCs into three parts, and then, artificial neural network (ANN) engines are used to forecast future values of the three parts. The load data of Alberta electricity market from 2000 to 2013 is used to verify validity of the proposed method.