Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

Elattar, Ehab E.; Elsayed, Salah K.; Farrag, Tamer Ahmed

doi:10.1109/access.2020.3048519

Cited by 22 publications

(16 citation statements)

References 49 publications

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“…In [28], the authors hybridized a cultural algorithm with HS to solve the constrained optimization problem of diesel blending. In [26], HS was combined with a neural network for forecasting electricity prices. A comprehensive review of the development of the HS algorithm along with hybrid versions and applications can be consulted in [38].…”

Section: Hybrid Hs-sa Algorithmmentioning

confidence: 99%

“…SA introduces diversification in the search process and allows the HS to escape from locally optimal solutions. The efficiency and benefits of HS have been proven in several applications of electrical engineering, including optimal network reconfiguration [23] and harmonic elimination [24]. Additionally, hybrid versions of HS have been proposed in the domains of data mining [25], electricity price forecasting [26], job-shop scheduling [27] and constrained optimization [28], among others.…”

Section: Introductionmentioning

confidence: 99%

“…The efficiency and benefits of HS have been proven in several applications of electrical engineering, including optimal network reconfiguration [23] and harmonic elimination [24]. Additionally, hybrid versions of HS have been proposed in the domains of data mining [25], electricity price forecasting [26], job-shop scheduling [27] and constrained optimization [28], among others. As regards the protection coordination problem, HS was implemented in [29][30][31] to approach the optimal coordination of directional OCRs.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Harmony Search Algorithm Applied to the Optimal Coordination of Overcurrent Relays in Distribution Networks with Distributed Generation

2021

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In recent years, distributed generation (DG) has become more common in modern distribution networks (DNs). The presence of these small-scale generation units within a DN brings new challenges to protection engineers, since short-circuit currents tend to increase; additionally, as with microgrids, modern DNs may feature several operational modes depending on their topology and the availability of DG. This paper presents a methodology for the optimal coordination of overcurrent relays (OCRs) in modern DNs with a high presence of DG. Given the fact that protection coordination is a non-linear and non-convex optimization problem, a hybrid harmony search and simulated annealing (HS-SA) approach was implemented for its solution and compared against other techniques, such as conventional HS, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid PSO-HS. Several tests were performed on a DN, considering different operative scenarios as a function of the DG available within the network. A comparison with other works reported in the specialized literature was carried out, evidencing the applicability and effectiveness of the HS-SA technique in solving the optimal OCR coordination problem in modern DNs.

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Section: Hybrid Hs-sa Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Harmony Search Algorithm Applied to the Optimal Coordination of Overcurrent Relays in Distribution Networks with Distributed Generation

2021

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“…In previous research, the AI technique has been implemented for the long-term load and PF of electricity conventionally. In [23][24][25][26], a short-term load and PF using an NN fitting tool were used to compute hourly and daily data of weather temperature and electricity load as input features. Furthermore, the generalized-RNN indicates temperature statistics and price signals as input parameters.…”

Section: Related Workmentioning

confidence: 99%

“…The time-series AR data have been considered an input to the proposed Feed-forward ANFIS and the overall output of Feed-forward ANFIS is computed in Equations ( 14)- (24). w 1 , w 2 , w 3 , w 4 , w 5 , and w 6 are the six best weights obtained from the ANFIS model.…”

Section: Pf Model Evaluationmentioning

confidence: 99%

A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

et al. 2021

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Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.

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Electricity price forecasting using hybrid deep learned networks

Singh

2023

Journal of Forecasting

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This paper presents a novel hybrid model integrating maximal overlap discrete wavelet transform (MODWT) denoising and empirical mode decomposition (EMD) with sequence‐to‐sequence (seq2seq) long short‐term memory (LSTM) neural networks for day‐ahead electricity price forecasting. The nonstationary and nonlinear time series electricity price data are first denoised using MODWT. The resulting signal is decomposed into several intrinsic mode functions (IMF) with different resolutions by EMD. The extracted IMF is then introduced into seq2seq LSTM to obtain an aggregated predicted value for electricity price. The proposed method is examined using the Nord pool Elspot energy market data. Empirical results show that the proposed model outperformed the other forecasting models like LSTM and stacked LSTM. The performance measures indicate that data denoising can significantly improve the prediction stability and the generalization ability of the LSTM model.

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Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

Cited by 22 publications

References 49 publications

Hybrid Harmony Search Algorithm Applied to the Optimal Coordination of Overcurrent Relays in Distribution Networks with Distributed Generation

Hybrid Harmony Search Algorithm Applied to the Optimal Coordination of Overcurrent Relays in Distribution Networks with Distributed Generation

A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

Electricity price forecasting using hybrid deep learned networks

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