Short Term Load Forecasting in Electric Power Systems with Artificial Neural Networks

Tsekouras, George J.; Kanellos, Fotios D.; Mastorakis, Nikos E.

doi:10.1007/978-3-319-15765-8_2

Cited by 14 publications

(15 citation statements)

References 58 publications

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“…The most significant data used for STLF are the hourly values of the load for time periods extending from past hours up to some weeks before the day the loads are going to be forecasted. Electric loads can be also attributed with temperature, weekday, season, etc . Therefore, loads in power system can be defined as dependent variables, while their next values can be predicted based on previous load values and an independent input vector.…”

Section: Power System State Transition Modelmentioning

confidence: 99%

“…Electric loads can be also attributed with temperature, weekday, season, etc. 31,32 Therefore, loads in power system can be defined as dependent variables, while their next values can be predicted based on previous load values and an independent input vector. The aforementioned characteristics for loads make it possible to use NARX network to forecast the loads.…”

Section: Power System State Transition Model With Nonlinear Autoregmentioning

confidence: 99%

“…Besides, it is verified by many researches that STLF with ANNs leads to mean absolute forecasting error (ε) ranging between 1.5 and 2.5%. 31,33 This level of accuracy is acceptable for describing dynamics of power system through discrete time transition model (or the process model), which is based on interdependency between loads and state vector.…”

Section: Power System State Transition Model With Nonlinear Autoregmentioning

confidence: 99%

See 2 more Smart Citations

Neural network-based power system dynamic state estimation using hybrid data from SCADA and phasor measurement units

Goleijani

Ameli

2017

Int Trans Electr Energ Syst

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Summary This paper points out the application of artificial neural network for short‐term load forecasting where the projected loads are utilized to define a discrete‐time state transition model (i.e., process model). The model is applied to estimate states dynamically and to generate pseudo measurements. Weights of neural network are not treated static and would be carried out under reevaluation alongside the estimation of state vector dynamically. The unscented Kalman filter estimation approach, which requires less approximation of power system, is used in the proposed method. The unscented Kalman filter is implemented through a dual structure due to the interactions of the state vector and the dynamic model of power system. The performance of the proposed method from accuracy prospective is compared with a couple of widely used methods. An optimum solution for wide‐area monitoring system would be realized through implementation of more realistic process model along the simplicity of the proposed method and its capability to handle hybrid measurement data from SCADA and phasor measurement units.

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Section: Power System State Transition Modelmentioning

confidence: 99%

Section: Power System State Transition Model With Nonlinear Autoregmentioning

confidence: 99%

Section: Power System State Transition Model With Nonlinear Autoregmentioning

confidence: 99%

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Neural network-based power system dynamic state estimation using hybrid data from SCADA and phasor measurement units

Goleijani

Ameli

2017

Int Trans Electr Energ Syst

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“…Reference [29] addresses issues regarding the forecasting of short term loads in electric power systems by means of artificial neural networks. The authors used feed-forward ANNs in order to achieve short-term predictions of the load demand in the national power system of Greece.…”

Section: Introductionmentioning

confidence: 99%

“…For example, comparing with [28], in which the authors do not consider the meteorological data and with [30], in which the authors consider only national and religious holidays, we have introduced some specific exogenous variables (meteorological and timestamps datasets) that influence the hypermarket's consumption. Comparing with [29], in which the authors have used the feed-forward ANNs in order to develop a short-term forecasting of the Greek Power System load demand, our method is based on ANNs developed based on the NAR and NARX models, that are more suitable in forecasting the time series' future terms.…”

mentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

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This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

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Load forecasting techniques for power systems with high levels of unmetered renewable generation: A comparative study

Foster

Liu²,

McLoone³

2018

IFAC-PapersOnLine

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Short Term Load Forecasting in Electric Power Systems with Artificial Neural Networks

Cited by 14 publications

References 58 publications

Neural network-based power system dynamic state estimation using hybrid data from SCADA and phasor measurement units

Neural network-based power system dynamic state estimation using hybrid data from SCADA and phasor measurement units

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

Load forecasting techniques for power systems with high levels of unmetered renewable generation: A comparative study

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