Artificial neural networks for short-term load forecasting in microgrids environment

Hernández, Leonardo; Baladrón, Carlos; Aguiar, Javier M.; Carro, Belén; Sánchez-Esguevillas, Antonio; Lloret, Jaime

doi:10.1016/j.energy.2014.07.065

Cited by 193 publications

(79 citation statements)

References 59 publications

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“…For a given value of E, the model creates a load response to a change in the DLMP according to (4). In this paper, both the SE and the CE of load response are considered and the combination of them can be defined in (5). To simplify the study, we consider the SE and CE as constants, which are calculated by analyzing the 24-h historical data in an actual DPS.…”

Section: B Price Elasticity Improvement Descriptionmentioning

confidence: 99%

“…As for the application of energy management, using load forecasting to regulate energy distribution in microgrid is very hot in recent research. The authors in [3]- [5] presented an electric load forecast architectural model to integrate distributed renewable sources. This is to balance the power generation of companies and demand of customers.…”

Section: Introductionmentioning

confidence: 99%

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A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Wang

Ouyang

Krishnan

et al. 2015

IEEE Trans. Ind. Inf.

135

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Abstract-Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.

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Section: B Price Elasticity Improvement Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Wang

Ouyang

Krishnan

et al. 2015

IEEE Trans. Ind. Inf.

135

View full text Add to dashboard Cite

show abstract

“…the neural net or support vector machine algorithms often just add such a holiday dummy. Also [42] indirectly use the 1) method, even they are introducing a 'working day' dummy which is zero on working days and non-zero on nonworking days. As they also incorporate Saturday and Sunday dummies, this approach spans up the same model space as the direct approach.…”

Section: Additional Public Holiday Dummiesmentioning

confidence: 99%

Modeling public holidays in load forecasting: a German case study

Ziel

2018

J. Mod. Power Syst. Clean Energy

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We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set, treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%.

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“…Since the 1980s, to improve the accuracy of load forecasting, many artificial intelligent (AI) approaches have been used and been combined to develop powerful forecasting methods, such as artificial neural networks (ANNs) [17][18][19][20][21], expert system-based methods [22][23][24], and fuzzy inference…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

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Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

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Artificial neural networks for short-term load forecasting in microgrids environment

Cited by 193 publications

References 59 publications

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Modeling public holidays in load forecasting: a German case study

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

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