Input variable selection for ANN-based short-term load forecasting

Drezga, I.; Rahman, Saifur

doi:10.1109/59.736244

Cited by 144 publications

(88 citation statements)

References 16 publications

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“…Usually, the number of consumption instances, prior to the value to be estimated, that one must take into account, is established in an arbitrary manner, based on experience obtained by using correlation analysis (Drezga, 1998 andHippert, 2001) (fig.4). What one must find out is whether the amount of contiguous information that is chosen is appropriate or whether it merely contributes to over-parameterize the model.…”

Section: Process Reconstruction and Memory Rangementioning

confidence: 99%

“…Thus, forecast plays a key role in this sector (Philipson, 1988). Several short-term load forecast (STLF) models have been developed in the last few decades (Drezga, 1998 andHippert, 2001). However, few amongst them have done a specific analysis of this sector (Chen, 1996, Fidalgo, 1999and Sargunaraj, 1997.…”

Section: Introductionmentioning

confidence: 99%

“…Other variables, of an auxiliary nature, are used and, not being directly related to electricity consumption, they are usually represented by functions of the sinusoidal or binary type with the goal of helping the ANN to detect periodic features of the load behaviour (Drezga, 1998 andFidalgo, 1999).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Short-term load forecast using trend information and process reconstruction

Santos

Martins

Pires

et al. 2006

Int. J. Energy Res.

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The algorithms for short-term load forecast (STLF), especially within the next-hour horizon, belong to a group of methodologies that aim to render more effective the actions of planning, operating and controlling electric energy systems (EES). In the context of the progressive liberalisation of the electricity sector, unbundling of the previous monopolistic structure emphasizes the need for load forecast, particularly at the network level. Methodologies such as artificial neural networks (ANN) have been widely used in next-hour load forecast. Designing an ANN requires, amongst other things, the proper choice of input variables, avoiding overfitting and an unnecessarily complex input vector (IV). This may be achieved by trying to reduce the arbitrariness in the choice of endogenous variables. At a first stage, we have applied the mathematical techniques of process-reconstruction to the underlying stochastic process, using coding and block entropies to characterize the measure and memory range. At a second stage, the concept of consumption trend in homologous days of previous weeks has been used. The possibility to include weather-related variables in the IV has also been analyzed, the option finally being to establish a model of the non-weather sensitive type. The paper uses a real-life case study.

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Section: Process Reconstruction and Memory Rangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-term load forecast using trend information and process reconstruction

Santos

Martins

Pires

et al. 2006

Int. J. Energy Res.

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“…Ho et al [36] perform a peak load forecast 24 h ahead; the same forecast is used by Ho et al [23], as input to an expert system that performs 24-hour ahead load forecasting. ANNs with one output can be repeatedly used to forecast load curves, as in [37,38] or by using a 24-hour parallel system, as shown McMenamin et al [39]. Lee et al [40] present a day divided into three periods having one ANN forecasting the load for each period.…”

Section: Introductionmentioning

confidence: 99%

“…Basically, [35][36][37][38][39]49,50] present peak load or aggregated daily predictions, which is a very useful parameter for instance for plant operations planning, but not detailed enough to perform other precise activities such as DR. For these, more detailed approaches calculating several predictions a day are required, in order to identify the nuances of the predicted load.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

et al. 2013

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Received : 28 November 2012; in revised form: 18 February 2013 / Accepted: 20 February 2013 / Published: 5 March 2013 Abstract: Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.

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Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems

El‐Hawary

2017

Advances in Electric Power and Energy Systems

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Input variable selection for ANN-based short-term load forecasting

Cited by 144 publications

References 16 publications

Short-term load forecast using trend information and process reconstruction

Short-term load forecast using trend information and process reconstruction

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems

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