Electric load forecasting: Literature survey and classification of methods

Alfares, Hesham K.; Nazeeruddin, Mohammad Khaja

doi:10.1080/00207720110067421

Cited by 578 publications

(312 citation statements)

References 89 publications

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“…A variety of short-term load prediction strategies designed for the transmission network level are presented in the literature, see e.g. [1,2] for reviews.…”

Section: Introductionmentioning

confidence: 99%

Effects of scale on load prediction algorithms

Tidemann

Høverstad

Langseth

et al. 2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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“…A variety of short-term load prediction strategies designed for the transmission network level are presented in the literature, see e.g. [1,2] for reviews.…”

Section: Introductionmentioning

confidence: 99%

Effects of scale on load prediction algorithms

Tidemann

Høverstad

Langseth

et al. 2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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“…Rigorous studies on the topic were conducted already in the 70's and 80's, resulting in a large number of forecasting methods as reported by Gross and Galina (1987). A more recent review and classification of the forecasting methods has been given by Alfares and Nazeeruddin (2002), where novel methods including fuzzy logic, genetic algorithms and networks (Hippert et al, 2001) have been included in addition to the conventional econometric models (Pindyck and Rubinfeld, 1997). These kinds of forecasting methods are commonly employed when there is little or no knowledge about the appliance stocks and other grass-root level consumer details (Zarnikau, 2003).…”

Section: Introductionmentioning

confidence: 99%

A model for generating household electricity load profiles

Paatero¹,

Lund²

2006

Int. J. Energy Res.

431

196

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SUMMARYElectricity consumption data profiles that include details on the consumption can be generated with a bottom-up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom-up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely leveled with 42% peak reduction and sudden three hour loss of load can be compensated with 61% mean load reduction.

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“…the difference between the spline and the observed data, see Eq. (2). We see that our transformation yields errors that follow a Normal distribution to a larger degree than the original.…”

Section: Methodsmentioning

confidence: 72%

“…As the grid becomes more dynamic with the introduction of various local power generators and storage units, predictions are essential to make cost-effective, real time decisions while avoiding voltage band violations. Various prediction strategies are presented in the literature; Bunn and Farmer [1] review the earlier load forecasting models while more recent approaches are summarized by Alfares et al [2].…”

Section: A Backgroundmentioning

confidence: 99%

Effects of data cleansing on load prediction algorithms

Høverstad

Tidemann

Langseth

2013

2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG)

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Abstract-The rollout of advanced metering infrastructure that is planned in many countries worldwide will lead to a massive inflow of data from moderately reliable sensory equipment. In principle, this will make intelligent and automated planning and operation possible at an increasingly finer scale in the electric grid. However, errors can creep into the meter data, either from faulty sensors or during transmission from the meters to the database. This work studies the role of data cleansing as a preprocessing step for short-term (24-hour) power load prediction. We focus on cleansing and prediction at several levels of granularity, from the transmission level via distribution substations down to single households.We believe that preprocessing filters such as cleansing should lead to more robustness and/or precision in the subsequent processing step. However, load cleansing frameworks tend to make the popular assumption of normally and independently distributed noise in the time series. We show that this is incorrect at the diurnal level, due to the characteristic pattern of power consumption, with two peak loads during daytime and a nighttime trough. Moreover, we present empirical evidence that a preprocessing step based on this assumption fails to contribute positively to the performance of the subsequent prediction step. To rectify this problem, we suggest to subtract the average power load consumption in a given period before cleansing. We present empirical evidence that this improves the robustness and efficiency of load cleansing as a preprocessing step. Data cleansing and load prediction is performed by a system that searches out parameters using an evolutionary approach.

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Electric load forecasting: Literature survey and classification of methods

Cited by 578 publications

References 89 publications

Effects of scale on load prediction algorithms

Effects of scale on load prediction algorithms

A model for generating household electricity load profiles

Effects of data cleansing on load prediction algorithms

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