Linear and non-linear methods for prediction of peak load at University of São Paulo

Sarduy, Julio R. Gómez; Santo, Katia Gregio Di; Saidel, Marco Antônio

doi:10.1016/j.measurement.2015.09.053

Cited by 19 publications

(14 citation statements)

References 24 publications

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“…Accurate peak load forecasting is so important that short term peak loads are often separately forecast, as in [29,30], for example. Peak load forecasting may be less sensitive to the choice of criterion than the forecasting of the full profile, but absolute maximum error (AME) was used in [30] to complement MAPE when comparing forecasting methods.…”

Section: Load Peak Sensitive Validationmentioning

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: Load Peak Sensitive Validationmentioning

confidence: 99%

Assessing and Comparing Short Term Load Forecasting Performance

et al. 2020

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“…Comparison with Autoregressive (AR) and an Autoregressive Moving Average (ARMA) models showed that the ANFIS model's results were better than those of AR and ARMA models. Using weather data, [11] developed and compared linear regression, artificial neural networks and ANFIS models for load prediction and found that ANFIS model gave more accurate results. For Canada's Ontario province, [12] used ANFIS to model electricity demand using data from the year 1976-2005.…”

Section: Related Workmentioning

confidence: 99%

Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption

Kasule

Ayan

2020

Sakarya University Journal of Science

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Uganda seeks to transform its society from a peasant to a modern and largely urban society by the year 2040. To achieve this, electricity as a form of modern and clean energy has been identified as a driving force for all the sectors of the economy. For this reason, electricity consumption forecasts that are realistic and accurate are key inputs to policy making and investment decisions for developing Uganda's electricity sector. In this study, we present an ANFIS long-term electricity forecasting model that is easy to interpret. We use the model to forecast Uganda's electricity consumption. The ANFIS model takes population, gross domestic product, number of subscribers and average electricity price as input variables and electricity consumption as the output. We use particle swarm optimization (PSO) algorithm and genetic algorithm (GA) to optimize the parameters of the model. A forecast accuracy of 94.34% is achieved for GA-ANFIS, while 90.88% accuracy is achieved for PSO-ANFIS as compared to 87.79% for multivariate linear regression (MLR) model. Comparison with official forecasts made by Ministry of Energy and Mineral Development (MEMD) revealed low forecast errors.

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“…For all networks the same properties were used, in order to have a reference for comparing the performance of all networks and select the most appropriate. The applied properties were obtained from a sensitivity analysis and addressed criteria reported in specialized literature [15], [18], [24]. The properties of networks are presented in table 2.…”

Section: B Stagementioning

confidence: 99%

“…In training phase, 70% of data were used, meanwhile the validation phase was carried out with 30% of the remaining data. This data selection method was applied in [24].…”

Section: B Stagementioning

confidence: 99%

Solar Radiation Prediction for Dimensioning Photovoltaic Systems Using Artificial Neural Networks

Angarita¹,

Santos²,

Duran³

et al. 2016

IJET

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this paper presents a prediction model of solar radiation for dimensioning photovoltaic generation systems in the Atlantic Coast of Colombia, using artificial neural networks. As a case of study is presented the municipality "El Carmen de Bolivar" located in this region. To obtain the model, the average data of daily temperature, relative humidity and solar radiation from the last ten years, reported by weather stations in this city were used. Six neural networks were designed with six variants of input variables (temperature, humidity and month) and the output variable (solar radiation). The best result was obtained using all input variables. In the training process, the correlation index (R) between solar radiation estimated by the model and the recorded data was 0.8. In validating the correlation index was 0.77.

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Linear and non-linear methods for prediction of peak load at University of São Paulo

Cited by 19 publications

References 24 publications

Assessing and Comparing Short Term Load Forecasting Performance

Assessing and Comparing Short Term Load Forecasting Performance

Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption

Solar Radiation Prediction for Dimensioning Photovoltaic Systems Using Artificial Neural Networks

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