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

Moradzadeh, Arash; Zakeri, Sahar; Shoaran, Maryam; Mohammadi‐Ivatloo, Behnam; Mohammadi, Fazel

doi:10.3390/su12177076

Cited by 106 publications

(54 citation statements)

References 52 publications

(44 reference statements)

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“…In order to attain these goals, scientists have used different methods and carried out numerous performance improvement techniques, such as: SVR [3,11,28], LSTM ANNs [3,4,10,11,26,28], RNN [5,28], BiLSTM ANN [5], Copula-DBN [7], DRNN-LSTM [8], CNN-RNN [9], the Prophet and Holt-Winters long-term forecasting models [13], CNNs [4,6,14,25], MLR [24,25], SVM [24], and ANFIS [24]. In contrast with these, within our article, we devised and developed a forecasting method for the hourly month-ahead electricity consumption based on a BiLSTM ANN enhanced with a multiple simultaneously decreasing delays approach coupled with FITNETs.…”

Section: Discussionmentioning

confidence: 99%

“…Analyzing the scientific literature, one can remark that when forecasting the electricity consumption, different forecasting time horizons are of interest, encompassing short- [3,14,19,22,24,26,28], medium- [19,21,26], and long-term timeframes [10,13,14,20,[23][24][25]64], each of them bringing their own particular advantages in line with the actual requirements and business needs of the contractors. We targeted the hourly month-ahead electricity prediction, considering the numerous benefits that such a forecast brings to the largeelectricity commercial center-type consumer, ranging from the negotiation and choosing of the most appropriate hourly billing tariffs and correct estimations for the month-ahead electricity consumption submitted to the dispatcher to proper decisional support in what concerns the return on investment in more energy efficient equipment and assessing expanding options.…”

Section: Discussionmentioning

confidence: 99%

“…In the research paper [3], Moradzadeh et al devised a hybrid method comprising of an adjusted support vector regression (SVR) and long short-term memory (LSTM) ANN, entitled SVR-LSTM, with a view to obtaining a short-term forecast of the electricity consumption of a microgrid located in Sub-Saharan Africa. For the input variables, the authors took into account different factors that have an effect on the microgrid's load; factors like the commercial establishments, different family units consisting of houses and their occupants, while for the outputs they used electricity load profiles.…”

Section: Literature Reviewmentioning

confidence: 99%

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Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Petroşanu

Pîrjan

2020

Sustainability

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The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Petroşanu

Pîrjan

2020

Sustainability

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“…Short-term load forecasting is a function that has been performed in various ways so far. However, in the previous studies, one can mainly point out some conventional procedures that have been presented to forecast the load, which are as follows: persistence, statistical, physical, artificial neural network (ANN), machine learning, deep learning, and hybrid techniques [12][13][14]. In a valuable study [15], a variety of data-driven techniques were introduced and employed in a comparative approach to solve the necessary forecasting problems in the power grid.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

Self Cite

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Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

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“…Studies to improve the stability of microgrids include applying machine learning techniques to operating prediction and probability-based systems and applying traditional methods such as control-theory-based distributed and cooperative control and energy storage system utilization methods. Recently, machine learning techniques have been applied to various fields, especially when learning time-series data, long short-term memory (LSTM), which is quite effective in long-term memory, has been used for load forecasting [28,29]. In [30], dynamic learning techniques applied to natural networks and population-based algorithms are also applied to the power prediction field for new and renewable sources.…”

mentioning

confidence: 99%

A Novel Structure of a Power System Stabilizer for Microgrids

Kim

Park

2021

Energies

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This paper proposes a novel structure of a power system stabilizer (PSS) to improve the stability of synchronous generators (SGs) in microgrids. Microgrids are relatively vulnerable in terms of stability due to their small size and low inertia. The rotational inertia and voltage support of SGs are highly suitable for getting over the vulnerabilities of microgrids, but there exist weaknesses in low-frequency oscillations (LFOs) and limitations of synchronization due to electromagnetic characteristics. Therefore, we study how to accommodate the features of microgrids in the PSS of SGs and further enhance present advantages. The PSS proposed in this paper not only damps out LFOs by conventional lead-lag compensation but also provides additional damping torque according to the magnitude of the perturbation using a synchronous impedance characteristic (SIC). The proposed Lyapunov energy-function-based control strategy can also increase the synchronizing power of the SG to improve transient stability. For performance verification, we use parameters obtained by the particle swarm optimization (PSO) algorithm to compare the existing PSS with the proposed one and analyze them. The effect of the proposed micro-power system stabilizer (µPSS) is analyzed through frequency response analysis, and finally, small-signal stability analysis and the performance of transient stability are verified by time-domain simulation (TDS) on MATLAB/Simulink.

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

Cited by 106 publications

References 52 publications

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

A Novel Structure of a Power System Stabilizer for Microgrids

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