Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

Moradzadeh, Arash; Sadeghian, Omid; Pourhossein, Kazem; Mohammadi‐Ivatloo, Behnam; Anvari‐Moghaddam, Amjad

doi:10.3390/su12083158

Cited by 48 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…indicate the efficiency of this hybrid model. Trained networks can be used as a toolbox to predict (1) new data, which is related to the future or at any time, and (2) test data, which is used at any time to forecast new data in the future. In the next step, test data are used to validate the training of each network.…”

Section: Input Variable Figure 5a Figure 5bmentioning

confidence: 99%

“…To face such problems and better demand response, power systems must increase their generation capacity. However, there are other problems, such as increased fossil fuel consumption and environmental pollutions [1,2]. As the energy crisis and the environmental crisis become more serious, Distributed Generations (DGs), as the main forms of Renewable Energy Sources (RESs), have attracted much attention in issues related to energy management and sustainability of the power systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

et al. 2020

Self Cite

View full text Add to dashboard Cite

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

show abstract

Section: Input Variable Figure 5a Figure 5bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…With this evaluation, the results of each study can be verified. Evaluating and ensuring the performance of results can be done in a variety of ways [36]. In this paper, the correlation coefficient (R), mean square error (MSE), root-mean-square error (RMSE) and mean absolute error (MAE) were used as statistical performance criteria to evaluate the performance of the GRNN method.…”

Section: Performance Evaluation Of Grnnmentioning

confidence: 99%

Machine Learning Based PEVs Load Extraction and Analysis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

show abstract

“…A diagram of the CHP-based system and its elements for preventive GMS is depicted in Figure 1. The network power and heat demands can be predicted by different methods, such as principal component analysis Moradzadeh and Garcia Marquez et al [38,39], neural networks Moradzadeh et al [40], deep learning Moradzadeh and Pourhossein [41], support vector machines Moradzadeh and Pourhossein [42], or other methods Moradzadeh and Khaffafi [43], and supplied by CHP units and the other units, including power-only and heat-only units. The electricity demand is supplied by power-only and CHP units, while the heat demand is supplied by heat-only and CHP units.…”

Section: Problem Formulationmentioning

confidence: 99%

Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

show abstract

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

Cited by 48 publications

References 39 publications

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Machine Learning Based PEVs Load Extraction and Analysis

Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

Contact Info

Product

Resources

About