Modeling electricity loads in California: ARMA models with hyperbolic noise

Nowicka-Zagrajek, Joanna; Weron, Rafał

doi:10.1016/s0165-1684(02)00318-3

Cited by 114 publications

(62 citation statements)

References 51 publications

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“…The largest differences occur during Christmas (December 24th-26th), but this can be improved by incorporating a holiday structure into the model. However, the prediction for the first 23 days in December is still much worse than the prediction obtained from a simple ARMA(3,3) model [17], i.e. the mean absolute deviation from the true values is 0.565 compared to 0.355 for the ARMA forecast.…”

Section: Modeling With Generalized Ornstein-uhlenbeck Type Processesmentioning

confidence: 71%

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Modeling electricity loads in California: a continuous-time approach

Weron

Kozlowska

Nowicka-Zagrajek

2001

Physica A: Statistical Mechanics and its Applications

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In this paper we address the issue of modeling electricity loads and prices with diffusion processes. More specifically, we study models which belong to the class of generalized Ornstein-Uhlenbeck processes. After comparing properties of simulated paths with those of deseasonalized data from the California power market and performing out-of-sample forecasts we conclude that, despite certain advantages, the analyzed continuous-time processes are not adequate models of electricity load and price dynamics.

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Section: Modeling With Generalized Ornstein-uhlenbeck Type Processesmentioning

confidence: 71%

“…[6,16]) over discrete models, further research will be in the direction of discrete time series models which offer a much better fit to market data [17].…”

Section: Modeling With Generalized Ornstein-uhlenbeck Type Processesmentioning

confidence: 99%

Modeling electricity loads in California: a continuous-time approach

Weron

Kozlowska

Nowicka-Zagrajek

2001

Physica A: Statistical Mechanics and its Applications

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“…Traditional statistical methods have the following disadvantages:  direct participation of analysts at their application;  complexity of calculations;  inadequate forecast accuracy;  the ability to apply only to a certain type of forecast;  sensitivity to input parameters [10,11]. Artificial neural networks are more preferable.…”

Section: Methodsmentioning

confidence: 99%

“…As a rule, the number of neurons in a hidden layer is determined experimentally. For this, it is necessary to put several experiments with different network configurations by the number of neurons in the hidden layer and compare the results of training [11][12][13].…”

Section: Methodsmentioning

confidence: 99%

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

2017

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Abstract.In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

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“…Electricity forecast model is divided into two major categories: top-down and bottom-up [12]. The top-down forecast approach uses huge historical data and the most forecasting methods such as regression, time series, fuzzy logic, neural network, and expert system belong to [15]. On the other hand, the bottom-up approach involves developing engineering modules and using it to capture data at appliance level [16] [17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Nigerian Electricity Generation Multi Year Tariff Order Pricing Model

Anosike¹,

Dara²,

Ngwaka³

et al. 2017

EPE

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The Multi Year Tariff Order (MYTO) is the Nigerian Electricity Regulatory Commission (NERC) pricing framework for determining the Nigerian Electricity Supply Industry (NESI) pricing model. One of the objectives of the NERC's MYTO pricing model is to ensure regulated electricity end user tariff without compromising return on investment. Achieving this objective is imperative to attract investors in the growing Nigerian electricity market. However, NESI has hitherto been faced with challenges ranging from its inability to provide sufficient power to its customers to not being viable enough to provide return on capital invested. In this paper, sensitivity analysis of power plant operation and performance parameters on the cost of electricity (CoE) generation using MYTO (power generation) pricing model were evaluated. Thermodynamic modeling and simulation of an open cycle gas turbine (OCGT) was carried out to augment scarce data on power plant performance and operation in Nigeria. Sensitivity analysis was carried out using probabilistic method based on Monte Carlo simulation (MCS) implemented in commercial software (@ Risk®). The result highlighted sensitivity of the model input parameters to cost of electricity generation based on technical and financial assumptions of MYTO model. Seven most influential parameters affecting generation cost were identified. These parameters and their correlation coefficients are given as: 1) foreign exchange rate, 0.76; 2) cost of fuel, 0.51; 3) thermal efficiency, −0.23; 4) variable operation and maintenance cost, 0.22; 5) fixed operating and maintenance cost, −0.03; 6) capacity factor, −0.02; and 7) average capacity degradation, 0.01. Based on the gas turbine engine and input parameter distributions statistics for this study, the generation cost lies between 9.84 to 15.45 ₦/kWh and the probabilities of CoE within these values were established.

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Modeling electricity loads in California: ARMA models with hyperbolic noise

Cited by 114 publications

References 51 publications

Modeling electricity loads in California: a continuous-time approach

Modeling electricity loads in California: a continuous-time approach

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

Analysis of Nigerian Electricity Generation Multi Year Tariff Order Pricing Model

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