Fuzzy Inference Systems Applied to LV Substation Load Estimation

Konjić, Tatjana; Miranda, Vladimiro; Kapetanovic, I.

doi:10.1109/tpwrs.2005.846210

Cited by 30 publications

(6 citation statements)

References 13 publications

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“…In a similar way, a range of different variables were considered in [121,123], including gender, age group, social class, and the number of other residents. The number of customers on an LV substation and the monthly energy consumption is used in [129]. In [110], the authors used the substation internal state in addition to lagged load, temperature and temporal features, which proved to be significant in improving the forecast.…”

Section: Econometric Variablesmentioning

confidence: 99%

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Haben,

Arora,

Giasemidis

et al. 2021

Preprint

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The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.

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Section: Econometric Variablesmentioning

confidence: 99%

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Haben,

Arora,

Giasemidis

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…1) The intercept should be zero as there would be no load if customer number is zero; 2) Although customers with distributed generation may produce inverse power flow, they are not included in the total customer classes in (8). All customer classes are assumed to contribute positive load to the substation load and therefore the coefficient in (8) should be non-negative; 3) Within each cluster, there are variances between the normalized templates and substations' load shapes.…”

Section: A Clusterwise Weighted Constrained Regressionmentioning

confidence: 99%

“…It takes advantage of the information of customers' bills to aggregate individual customer's peak to substation peak. Based on this concept, recent work adopts advanced techniques including fuzzy regression [7], fuzzy inference [8] and artificial neural network (ANN) [9] to handle uncertainties, narrow confidence intervals and improve the accuracy. However, all these methods do not consider the diversifications in customer composition and structures of LV substations.…”

Section: Introductionmentioning

confidence: 99%

Development of Low Voltage Network Templates—Part II: Peak Load Estimation by Clusterwise Regression

et al. 2015

IEEE Trans. Power Syst.

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This paper proposes a novel contribution factor (CF) approach to predict diversified daily peak load of low voltage (LV) substations. The CF for each LV template developed in part I of the paper is determined by a novel method-clusterwise weighted constrained regression (CWCR). It takes into account the contribution from different customer classes to substation peaks, respecting the natural difference in time and magnitude between LV substation peaks and the variance within the templates. In CWCR, intercept and coefficients are constrained to ensure that the resultant coefficients do not lead to reverse load flow and can respect zero-load substations. Cross validation is developed to validate the stability of the proposed method and prevent over fitting. The proposed method shows significant improvement in the accuracy of peak estimation over the current status quo across 800 substations of different mixes of domestic, industrial and commercial (I&C) customers. The work in the two parts of the paper is particularly useful for understanding the capabilities of LV networks to accommodate the increasing penetration of low carbon technologies without large-scale monitoring.

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“…Beccali et al [8] used the output of the SOM as an input variable for the NN; whereas Fan and Chen [15] used the output of the SOM to call a specific set of support vector regression models. Espinoza et al [16], Konjic et al [17] and Sousa et al [10] clustered demand profiles of customer demographics and applied the known demand profiles to other customers in order to produce short-term demand forecasts. As noted by Hippert [7], machine learning techniques such as NNs can be used specifically for pattern recognition.…”

Section: Demand Profile Forecastingmentioning

confidence: 99%

Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system

Bennett

Stewart

2014

Energy

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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 models • Variables included demand lags, moving average forecast, temperature and humidity Forecasting LV distribution network demand profiles using a pattern recognition based expert system Abstract The advent of distributed renewable energy supply sources and storage systems has placed a greater degree of focus on the operations of the Low Voltage (LV) electricity distribution network.However, LV networks are characterised by having much higher variability in time series demand meaning that modelling techniques solely relying on iterative forecasts to produce a next day demand profile forecast are insufficient. To cater for the complexity of LV network demand, a novel hybrid expert system comprised of three modules, namely, correlation clustering, discrete classification neural network, and a post-processing procedure was developed. The system operates by classifying a set of key variables associated with a future day and refining a recalled historical demand profile as the forecast. The expert system exhibited high hindcast accuracy when trained with a residential LV transformer's demand data with R 2 values ranging from 0.86 to 0.87 and MAPE ranging from 11% to 12% across the three phases of the network. Under simulated real world conditions the R 2 statistic reduced slightly to 0.81-0.84 and the MAPE increased to 12.5-13.5%. Future work will involve integrating the developed expert system for forecasting next day demand in an LV network into a comprehensive distributed energy resource management algorithm.

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Fuzzy Inference Systems Applied to LV Substation Load Estimation

Cited by 30 publications

References 13 publications

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Development of Low Voltage Network Templates—Part II: Peak Load Estimation by Clusterwise Regression

Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system

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