Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Taylor, James W.; McSharry, Patrick E.

doi:10.1109/tpwrs.2007.907583

Cited by 492 publications

(251 citation statements)

References 22 publications

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“…For example, paper [1] follows a double seasonal exponential smoothing for half hour data and predict with mean average percentage error (MAPE) 1.25 to 2 percent. In paper [2] overall forecast result is about MAPE 1.5 to 3 percent for the dataset of 10 European countries. But, for proper prediction only the historical load data may not sufficient because there are several other important factors that cause instant variation on demand of load.…”

Section: Introductionmentioning

confidence: 86%

Short-term Electricity Load Forecasting Model and Bayesian Estimation for Thailand Data

Chapagain

2016

MATEC Web of Conferences

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Abstract. This paper proposes multi-equation linear regression model with autoregressive AR(2) method for modelling and forecasting a day ahead electricity load. AR(2) is used to show the dependency of next data on its previous two days data because the nature of electricity load consumption for the next day follow the pattern of previous days. Since, we allocate one equation for particular half hour, we need 48 separate equations to predict for one complete day. Parameters of model are estimated based on two different approaches-(i) Classical approach, and (ii) Bayesian approach. Classical or Ordinary Least Square approach estimates the parameters in terms of single value and hence its forecast is also single value, where as Bayesian approach includes the predictive distribution of electricity load due to multiple values of parameters. So, we can forecast electricity load not only from mean, but median and with other percentile values. In this paper, we use 70-percentile value for forecasting because the performance for all models accounts better in this percentile than that of mean and median forecast. Finally, we compare the performances where Bayesian estimation provides better and consistent performance than that of OLS estimation.

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Section: Introductionmentioning

confidence: 86%

Short-term Electricity Load Forecasting Model and Bayesian Estimation for Thailand Data

Chapagain

2016

MATEC Web of Conferences

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“…Small scale integration is often used for prognozing the time series [15], that is to say predicting not the series itself or the process, but its change or accession. In other words we get the series 1…”

Section: Mathematical Modelmentioning

confidence: 99%

Modeling prices of wholesale market of electric energy and power by the example of the UPS of the Ural

Mokhov

Demyanenko

2017

SHS Web Conf.

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Abstract. The article oversees forecasting model for deviations of the balancing market index and dayahead market index according to the maximum similarity sample for different levels of approximation in the context of positive and negative time-series value. The model was being tested on the factual data of the Integrated Power system of the Ural, Wholesale market for electricity and power of Russian Federation. Describes the price formation on the day-ahead market and the balancing market index. The necessity to use accurate forecasting methods consumption and prices of electrical energy and power to reduce penalties when the electric power industry entities on the energy exchange. The testing of mathematical models to predict the balancing market index deviations and day-ahead market based on a sample of maximum similarity with certain approximation equations for positive and negative values gave the prediction error of 3.3%.

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“…Another way is to establish a high-accuracy three-dimensional atmospheric physical model and calculate the wind speed and wind direction of each wind turbine based on the boundary conditions of the wind farm, which can finally be transferred to wind power of wind farm [5]. The main methods of statistical prediction are time series method [6], neural network [7], Support Vector Machine (SVM) [8], etc. The time series method establishes a time series model based on a large number of historical data and forecasts wind power with the time series model.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

et al. 2016

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Abstract. Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV) method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA) is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.

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Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Cited by 492 publications

References 22 publications

Short-term Electricity Load Forecasting Model and Bayesian Estimation for Thailand Data

Short-term Electricity Load Forecasting Model and Bayesian Estimation for Thailand Data

Modeling prices of wholesale market of electric energy and power by the example of the UPS of the Ural

A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

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