Notice of Violation of IEEE Publication Principles: Short-term load forecasting methods: A review

Srivastava, Ajay Kumar; Pandey, Ajay Shekhar; Singh, Devender

doi:10.1109/iceteeses.2016.7581373

Cited by 55 publications

(11 citation statements)

References 63 publications

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“…In addition, electricity prices and demand generally have a complicated structure. To minimize risk and maximize pro ts, electricity market participants are highly interested in predicting future electricity prices and demand [1,2]. In today's electricity markets, participants are generally interested in various forecasting horizons, including short, medium, and long term.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches

Shah

Iftikhar

Ali

2022

Journal of Mathematics

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Electricity demand and price forecasting are key components for the market participants and system operators as precise forecasts are necessary to manage power systems effectively. However, forecasting electricity demand and prices are challenging due to their specific features, such as high frequency, volatility, long trend, nonconstant mean and variance, mean reversion, multiple seasonalities, calendar effects, and spikes/jumps. Thus, the main aim of this study is to propose models that can efficiently forecast electricity demand and prices. To this end, the time series (demand/price) is divided into two components. The first component is considered a deterministic component that includes a trend, yearly, seasonal, and weekly periodicities, calendar effects, and lagged exogenous information and is modeled by parametric and nonparametric approaches. The second component is known as a stochastic (residual) component that is estimated using univariate autoregressive (AR) and multivariate vector autoregressive (VAR) models. The estimation of these models is carried out by four different estimation methods, including ordinary least squares (O), Lasso (L), Ridge (R), and Elastic-net (E). The proposed modeling scheme is applied to Nordic electricity demand and price time series, and one-day-ahead out-of-sample forecasts are obtained for a whole year. Besides descriptive statistics, a statistical significance test is also used to evaluate the models’ forecasting accuracy. The results suggest that the proposed methodology effectively forecasts the price and demand for electricity. In addition, the choice of the estimation procedure used for both deterministic and stochastic components has a significant effect on the forecasting results. Furthermore, multivariate vector autoregressive gives superior performance compared to univariate autoregressive models.

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Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches

Shah

Iftikhar

Ali

2022

Journal of Mathematics

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“…Load forecasting is broadly classified into three categories: the short-term load forecasting which forecasts from one hour to seven days, the medium-term load forecast starting from a week to several months, and long-term load forecasting starting from a year to several years. The short-term load forecasting is used by the supply authority for operational purposes such as unit commitment, economic dispatch, load flow, frequency control, security, and reliability of the system (Srivastava et al, 2016). The medium-term load forecasting predicts the load demand that provides information for system planning and operation while long term load forecasting is mainly used for power system planning (Ganguly et al, 2019;Peng et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Ibrahim¹,

Owonikoko²,

Abdulkarim³

et al. 2021

JST

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A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

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“…For example, the methodology of variable selection may be applicable to both MLR models or ANNs (Hong and Fan, 2016). Several techniques and methodology are used in load forecasting, and many papers review these methods (Hong and Fan, 2016) (Srivastava et al, 2016). Some papers simply present several techniques and make a comparison between them (Liu et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…Some papers simply present several techniques and make a comparison between them (Liu et al, 1996). Hybrid forecasting is a commonly used model that combines two or more different techniques to overcome the drawbacks of using one method (Srivastava et al, 2016). One of the hybrid model suggested is the FLa neural network module which is used for building blocks able for constructing a more reliable load forecasting system (Srinivasan and Liew, 1995).…”

Section: Introductionmentioning

confidence: 99%

A new load forecasting model considering planned load shedding effect

Alkaldy

Albaqir

Hejazi

2019

IJESM

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Purpose Load forecasting is important to any electrical grid, but for the developing and third-world countries with power shortages, load forecasting is essential. When planed load shedding programs are implemented to face power shortage, a noticeable distortion to the load curves will happen, and this will make the load forecasting more difficult. Design/methodology/approach In this paper, a new load forecasting model is developed that can detect the effect of planned load shedding on the power consumption and estimate the load curve behavior without the shedding and with different shedding programs. A neuro-Fuzzy technique is used for the model, which is trained and tested with real data taken from one of the 11 KV feeders in Najaf city in Iraq to forecast the load for two days ahead for the four seasons. Load, temperature, time of the day and load shedding schedule for one month before are the input parameters for the training, and the load forecasting data for two days are estimated by the model. Findings To verify the model, the load is forecasted without shedding by the proposed model and compared to real data without shedding and the difference is acceptable. Originality/value The proposed model provides acceptable forecasting with the load shedding effect available and better than other models. The proposed model provides expected behavior of load with different shedding programs an issue helps to select the appropriate shedding program. The proposed model is useful to estimate the real demands by assuming load shedding hours to be zero and forecast the load. This is important in places suffer from grid problems and cannot supply full loads to calculate the peak demands as the case in Iraq.

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Notice of Violation of IEEE Publication Principles: Short-term load forecasting methods: A review

Cited by 55 publications

References 63 publications

Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches

Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

A new load forecasting model considering planned load shedding effect

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