Advancement in the application of neural networks for short-term load forecasting

Peng, Tie-Mao; Hubele, Norma Faris; Karady, G.G.

doi:10.1109/59.141711

Cited by 332 publications

(80 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is proved by one another author also that artificial neural network models have learning ability (Kalogirou, 2000) and with this ability these models perform much better than other classical model many times. May be that is why Dillon et al (1991) and Peng et al (1992) didn't compare the outcomes comparatively. Moreover, wavelet transformation and neural network model was utilized by Yao et al (2000) in order to forecast electrical load.…”

Section: Forecasting the Electricity Demand And Strategic Planningmentioning

confidence: 99%

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

Demir

Özsoy

2014

IJAREMS

View full text Add to dashboard Cite

Electricity is a kind of energy that consumption increases rapidly. However, Electricity is used from the residences to the industry everywhere. This non-storable energy needs to be predicted precisely and supplied sufficiently. For this reason in this research electricity demand of Georgia is predicted using comparative models as Box Jenkins and Neural Network. By this way, an accurate model was developed to perform a strategic plan. For the calculation of significance level, MAPE is utilized to see error. It was seen that artificial neural network multilayer perceptron model provided better predictions rather than SARIMA model. Finally, some suggestions are expressed to the government of Georgia for future plans.

show abstract

Section: Forecasting the Electricity Demand And Strategic Planningmentioning

confidence: 99%

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

Demir

Özsoy

2014

IJAREMS

View full text Add to dashboard Cite

show abstract

“…Using neural networks which combine weather data and historical loads with non-linear curve-fitting has been popular [30,48,36]. Curve fitting was also used with fuzzy logic techniques [22,4] which avoids advanced mathematical models.…”

Section: Other Machine Learning Techniquesmentioning

confidence: 99%

“…Further, other machine learning techniques have been applied such as non-linear curve-fitting [48,36] and Support Vector Machines (SVM) [23,19]. It should be noted here that the load forecasting is significantly different from peak prediction [30] [37].…”

Section: Internal Structurementioning

confidence: 99%

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Abstract.Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. Such peaks can be regulated in smart energy grids with the introduction of relatively simple techniques such as load balancing and smart pricing strategies. This is, however, difficult in practice because it requires prediction of peaks prior to their actual occurrence.While most studies formulate the problem as an estimation problem, we take a radically different approach and formulate it as a classical pattern recognition problem. Further, the paper applies classification methods to solve the problem and applies these with real-life data from a Norwegian smart grid pilot project. Some of the key findings are that the algorithms can accurately detect 80% of energy consumption peaks up to one week ahead of time. Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion.

show abstract

“…The approach in this study is a simple but also very effective tool for forecasting problems since it has the ability to approximate any nonlinear function. Due to its datadriven properties, it is also able to solve problems where the input-output relationship is neither well defined nor easily computable [3][4][5][6][7][8]. According to the literature, the single hidden layer feed-forward network [9] is the most popular model for time series modeling and forecasting.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

Özgüner¹,

Tör²,

Güven³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

This study presents a system day-ahead hourly market clearing price forecasting tool for the day-ahead (DA) market and a system DA hourly marginal price forecasting tool for the real-time market of the Turkish electric market (TEM). These forecasting tools are developed based on artificial neural networks (ANNs). A series of historical price data of the TEM are utilized to model and optimize the ANN structure and to develop the ANN-based price forecasting tool.The methodology used to select the optimum ANN architecture provides the minimum daily mean absolute percentage error for both day-ahead market prices in the TEM. Performances of the proposed ANN model and the multiple linear regression model in forecasting the day-ahead hourly market clearing price are compared. The proposed ANN model is modified using volatility analysis and the Bienayme-Chebyshev inequality in order to forecast system marginal prices probabilistically within a lower and an upper boundary.

show abstract

Advancement in the application of neural networks for short-term load forecasting

Cited by 332 publications

References 15 publications

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

Forecasting the Monthly Electricity Demand of Georgia using Competitive Models and Advices for the Strategic Planning

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

Contact Info

Product

Resources

About