2014
DOI: 10.1016/j.energy.2014.01.032
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Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system

Abstract: J-W. (2014) Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system. Energy, (on-line in-press Elsevier), http://dx. Research Highlights• Pattern recognition expert system to forecast demand profiles of an LV transformer • Incorporates ARIMAX forecasts, correlation clustering and NN discrete classification • LV transformer load influenced by temperature, humidity and day of the week • Used ARIMA modelling method with external variables to construct ARIMAX mo… Show more

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Cited by 53 publications
(39 citation statements)
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“…Generally, the past literature has adopted traditional methods such as linear regression [17], expert systems [18], neural networks [19,20], and Markov prediction and factor analysis [21] for yield prediction. However, the biofuels system is a complex system, which is easily affected by various factors such as the economy, resources, and social issues.…”
Section: Problems With Predicting Productionmentioning
confidence: 99%
“…Generally, the past literature has adopted traditional methods such as linear regression [17], expert systems [18], neural networks [19,20], and Markov prediction and factor analysis [21] for yield prediction. However, the biofuels system is a complex system, which is easily affected by various factors such as the economy, resources, and social issues.…”
Section: Problems With Predicting Productionmentioning
confidence: 99%
“…The forecasting results indicate that the proposed model outperforms other compared models.Mathematics 2019, 7, 1188 2 of 23 various statistical models that contain the ARIMA models [6][7][8], regression models [9][10][11], exponential smoothing models [12,13], Kalman filtering models [14,15], Bayesian estimation models [16,17], and so on. However, the inherent shortcomings of these statistical models are that they are only defined to deal with the linear relationships among the electricity consumption and other influenced factors mentioned above, eventually, only receiving unsatisfied forecasting results [18].Along with advanced nonlinear computing ability, the AI models have been mature diffusely explored to improve the forecasting accuracy of electricity consumption since the 1980s, such as artificial neural networks (ANNs) [18][19][20][21][22], expert system models [23][24][25][26], and fuzzy inference methodologies [27][28][29][30]. To further overcome the inherent drawbacks embedded in these AI models, hybrid and combined models (hybridizing or combining with other advanced AI techniques) have received lots of attention [31][32][33][34][35][36][37][38][39], as mentioned in [5], three kinds of these hybrid or combined models, for example, hybridizing or combining these AI models with each other [31][32]…”
mentioning
confidence: 99%
“…The load profile forecasting approach used in the test site University building N44 control system follows the general method of [7] with forecast models modified to suit commercial building scenarios.…”
Section: Forecasting Algorithmmentioning
confidence: 99%
“…The optimal operating schedule of the storage device is obtained by maximizing an objective function which corresponds to the maximum benefit for the storage owner [6]. A forecasting algorithm for a BESS is used to determine when a peak load is likely to occur, how long that peak load will be occurring for and what the power requirement of that peak load is [7]. This is important information for a scheduling system that is required to best utilize the energy stored within the BESS.…”
Section: Introductionmentioning
confidence: 99%