A bat optimized neural network and wavelet transform approach for short-term price forecasting

Bento, Pedro; Pombo, J.A.N.; Calado, M.R.A.; Mariano, S.J.P.S.

doi:10.1016/j.apenergy.2017.10.058

Cited by 103 publications

(62 citation statements)

References 45 publications

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“…Therefore, in the case of continuous distribution, the probability that different retailers have same quote is zero, that is, P (P i = P j ) = 0. Suppose the BNE is [P i , P j ] (I = j) when the profit of retailer is maximized, that is, for each C i ∈T i , P i (C i ) satisfies Equation (14).…”

Section: Optimal Bidding Strategies Under Different Probability Distrmentioning

confidence: 99%

“…. Therefore, in the case of continuous distribution, the probability that different retailers have the same quote is zero, so the objective function is still Equation (14). However, the distribution density function becomes complicated at this time.…”

Section: Of 19mentioning

confidence: 99%

“…correlation, and wavelet analysis in a pre-processing stage. Afterwards a feedforward neural network was used alongside Bat and Scaled Conjugate Gradient Algorithms to improve the traditional neural network learning capability [14]. Neural network is the most used method for short-term EPF.…”

Section: Introductionmentioning

confidence: 99%

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Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

et al. 2018

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The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users' historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on "meteorological similarity day (MSD)" to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm-back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer's bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.In 2014 alone, the power trade with EEX reached an astonishing 1952 tWh. The Pennsylvania New Jersey Maryland (PJM) exchange in the USA is currently responsible for the operation and management of power systems in 13 U.S. states and the District of Columbia. As a regional Independent system operation (ISO), PJM is responsible for centrally dispatching the largest and most complex power control area in the USA and ranks third in the world in scale. The PJM controlled area accounts for 8.7% of the total population (about 23 million), load 7.5%, installed capacity of 8% (about 58,698 MW), and transmission lines up to more than 12,800 kilometers [4]. The effective application of power big data for the profitability and control of power companies has a high value. Some experts have said that whenever the data utilization increases by 10%, it can cause the power grid to increase profits by 20% to 49%. In the face of a huge power system, data needs to be processed quickly, hence data mining technology came into being, and will play a key role in supporting the sales company to participate in market competition.Power-selling is the pillar business of the retailer. As an intermediate link between production and use, the power retailer needs to analyze the users' needs and predict the user's load demand based on the users' historical electricity consumption data, considering industry, meteorology, regional economy, related industries, and other factors, so that this can guide the retailer to develop electricity purchase. In recent years, numerous scholars have put forward many effective load forecasting methods. Vilar, J. et al. provided two procedures to obtain predi...

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Section: Optimal Bidding Strategies Under Different Probability Distrmentioning

confidence: 99%

Section: Of 19mentioning

confidence: 99%

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Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

et al. 2018

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“…However, the BPNN model still has some intrinsic defects, for example, slow convergence speed and over-fitting problem [40][41][42][43]. Fortunately, a large collection of optimization algorithms have been developed to optimize the BPNN model, such as GA [44,45], MEA [46], particle swarm optimization (PSO) [47,48], simulated annealing (SA) [49], bat algorithm (BA) [50,51], etc. Among them, evolutionary algorithms, such as GA and MEA, have recently been widely used as optimization algorithms searching the optimal weights and thresholds of neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Zhang

2019

Sustainability

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The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

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“…10. Table 2 shows the result of proposed model in compare of simple NN and combination of NN and bat algorithm of [18] for each clusters by simple and improved MAE.…”

Section: Clustermentioning

confidence: 99%

An intelligent model to predict the day-ahead deregulated market clearing price: a hybrid NN, PSO and GA approach

et al. 2018

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A bat optimized neural network and wavelet transform approach for short-term price forecasting

Cited by 103 publications

References 45 publications

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

An intelligent model to predict the day-ahead deregulated market clearing price: a hybrid NN, PSO and GA approach

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