An implementation of a neural network based load forecasting model for the EMS

Papalexopoulos, A.; Hao, Shangyou; Peng, Tie-Mao

doi:10.1109/59.331456

Cited by 213 publications

(80 citation statements)

References 13 publications

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“…To the best of our knowledge, there has not been a study in the Greek electricity market involving PCA or the augmented ARMA model, SARIMAX. We have also chosen the ANN models that have been used extensively in the GEM (e.g., [14,44,45]), with known pros and cons. SARIMAX models however have not been used (although ARMA and ARIMA ones have been applied) [126].…”

Section: Discussionmentioning

confidence: 99%

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Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

et al. 2016

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Abstract:In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004-2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

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Section: Discussionmentioning

confidence: 99%

“…The model can forecast load profiles from one to seven days. Papalexopoulos et al [45] developed and applied a multi-layered feed forward ANN for short-term system load forecasting. Season related inputs, weather related inputs and historical loads are the inputs to ANN.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

et al. 2016

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“…While the solutions studied in the literature [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][55][56][57][58][59] present sometimes good prediction efficiency figures (normally their MAPEs are around 2%), they deal almost exclusively with big areas, and mainly entire countries, and they are never applied to smaller environments of the size of small cities or microgrids. Therefore, they do not give any evidence of how will they behave when applied to highly variable load curves.…”

Section: Geographical Area In Load Forecastingmentioning

confidence: 99%

“…Lu et al [41] conducted an experiment with three ANN models of two utilities, and conclude that systems are dependent and must be adjusted to each of the utilities. With Papalexopoulos et al [42], temperature is represented by non-linear functions, which are used as input, and suggests a set of measures to improve load performance in public holidays. Barkitzis et al [43] present an improved model that considers public holidays.…”

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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“…The most important type of variable included in the input vector (IV) is the past timeseries of the variable being forecast (Hippert, 2001, Senjyu, 2002, Papalexopoulos, 1994and Khotanzad, 1994. 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%

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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An implementation of a neural network based load forecasting model for the EMS

Cited by 213 publications

References 13 publications

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

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

Short-term load forecast using trend information and process reconstruction

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