Assessment of some methods for short-term load forecasting

Koponen, Pekka; Mutanen, Antti; Niska, Harri

doi:10.1109/isgteurope.2014.7028901

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…They compared linear regression, support vector regression (SVR) and multilayer perceptron (MLP) in this respect. [26] shows a typical short-term load forecasting accuracy dependence on the prediction time horizon. The weather forecasts and load forecasting methods have improved much so now the accuracy decreases somewhat later but the shape of the dependency is still similar.…”

Section: Comparison Of Methods Across Different Forecasting Casesmentioning

confidence: 99%

Assessing and Comparing Short Term Load Forecasting Performance

et al. 2020

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When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It compares typically applied criteria for tuning and performance assessment of load forecasting methods with estimated costs caused by the forecasting errors. The focus is on short-term forecasting of the loads of energy systems. The estimated costs comprise electricity market costs and network costs. We estimate the electricity market costs by assuming that the forecasting errors cause balancing errors and consequently balancing costs to the market actors. The forecasting errors cause network costs by overloading network components thus increasing losses and reducing the component lifetime or alternatively increase operational margins to avoid those overloads. The lifetime loss of insulators, and thus also the components, is caused by heating according to the law of Arrhenius. We also study consumer costs. The results support the assumption that there is a need to develop and use additional and case-specific performance criteria for electricity load forecasting.

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Section: Comparison Of Methods Across Different Forecasting Casesmentioning

confidence: 99%

Assessing and Comparing Short Term Load Forecasting Performance

et al. 2020

Self Cite

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“…In addition to cluster profiles, individual load profiles are calculated for those large customers that exhibit unique consumption characteristics. Seasonal temperature dependencies are calculated for every cluster and individual profile and when combined with outdoor temperature forecasts, the new load profiles can be used also for load forecasting [4].…”

Section: Clustering and Load Profilingmentioning

confidence: 99%

“…During the SGEM project, several different models utilizing hourly metered consumption data were evaluated for shortterm load forecasting. The studied models were; a cluster profile based predictor, a Kalman-filter based predictor with input nonlinearities and physically based main structure, a neural network (NN) model [4], and a support vector machine (SVM) model [8]. The NN and SVM models were the most accurate, but also the other methods had their relative merits.…”

Section: Load Forecastingmentioning

confidence: 99%

Mining smart meter data - case Finland

Mutanen¹,

Niska²,

Järventausta³

2016

CIRED Workshop 2016

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Smart meters collect a lot of data on customer level electricity consumption and this, together with other data sources e.g. environmental information and public open data, provides an excellent basis for data mining. As a part of a recent smart grid project conducted in Finland, several different ways of mining smart meter data were studied. The project brought advances in customer classification and clustering, load profiling, spatial load analytics, behaviour change detection and load forecasting.

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