Long-term/mid-term electric load forecasting based on short-term correlation and annual growth

Al-Hamadi, H.M.; Soliman, S. A.

doi:10.1016/j.epsr.2004.10.015

Cited by 166 publications

(69 citation statements)

References 5 publications

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“…There are a large number of influential that characterize and directly or indirectly affect the underlying forecasting process [4]. However, neither the accurate amount of needed power nor the preparation for such amounts of power is as easy as it looks.…”

Section: Introductionmentioning

confidence: 99%

“…The reasons are: 1) Long-term load forecasting is always inaccurate as the years increase 2) Peak demand is very much dependant on weather condition 3) Unavailability of weather and economical data 4) It is very difficult to store electric power with the present technology, 5) It takes several years and requires a great amount of investment to construct new power generation stations and transmission facilities [4]. Therefore, any long-term load demand forecasting, by nature, is inaccurate.…”

Section: Introductionmentioning

confidence: 99%

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Long Term Load Forecasting of Jimma Town for Sustainable Energy Supply

Minaye¹,

Matewose²

2016

IJSR

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Now a day, due to economic development of the country the electrical energy demand of Ethiopia increases by 30% yearly.But in order to satisfy customers' power demand the planning and expansion of power must be done using a proper load forecasting methods. Due to this demand forecasting is a vital and integral process for planning periodical operations and facility expansion in the electricity sector. Even though demand forecasting is a central process its' pattern is almost very complex due to the deregulation of energy markets. Therefore, finding an appropriate forecasting model that will generalize the demand patter for a specific electricity network may not be an easy task. This paper presents a realistic methodology that can be used as a guide to construct Jimma town Electric Power Load Forecasting models. Trending methodology statistical analyses are involved to study the load features and forecasting precision, such as linear regression, compound growth model and quadratic regression. Real monthly and yearly load data from Jimma distribution system substation is used as a case study. By using the best optimal value of the rank correlation coefficient and mean absolute percentage error, the compound growth model is used in the coming five years load forecasting. By forecasting of Jimma town load demand will result in proper utilization of energy and for planning of any electricity related projects it will use as a baseline to be applied cost wise. The main objective of this study is to assess the future energy demand of customers so that supplying of power and using that without shortage will be optimized.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long Term Load Forecasting of Jimma Town for Sustainable Energy Supply

Minaye¹,

Matewose²

2016

IJSR

View full text Add to dashboard Cite

show abstract

“…To improve the accuracy of annual electric load forecasting, many approaches have been proposed by scholars and practitioners in the past decades, such as time series technology and regression models [2][3][4][5][6]. However, it is difficult to achieve significant improvements in terms of forecasting accuracy with these forecasting methods due to their poor non-linear fitting capability.…”

Section: Introductionmentioning

confidence: 99%

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

et al. 2012

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Abstract:The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.

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“…In the present study, the focus is on Mid-long term load forecasting, as it directly influences major energy investment decisions, such as the development of new power plans. Traditionally, statistical models-such as linear regression [2,3] and auto-regressive models [4]-have been widely used in practice for load forecasting, principally because of their simplicity and good performance ratios [5]. However, in recent years, various other models have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Mid-Long Term Load Forecasting using Multi-Model Artificial Neural Networks

Nezzar¹,

Farah²,

Khadir³

et al. 2016

ijeei

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Electrical load is a major input factor in a country's economic development. To support economic growth and meet future demands for electricity, Load forecasting has become a very important task for electrical power management and planning. Several techniques have been employed to accomplish this task. One of those that is mostly used is Artificial Neural Networks (ANNs) method, which have seen the largest number of studies in the field. the load time series data are auto-correlated and are influenced by other variables, such as temperature and population, etc. Furthermore, a large data sample is needed to perform mid-long term forecasts. For the latter purpose and in order to overcome both the lack of data and the high complexity of adding exogenous variables, a multi-model method, based on neural networks, is proposed for the mid-long term load consumption forecasting. The proposed model decomposes, the initial time series into three components using the X12-ARIMA algorithm, and performs a forecast for each component using three Nonlinear Auto Regressive (NAR) neural networks, with a Feed Forward neural network combining the outputs of the NARs. The experiments that were conducted showed that the proposed model provided good performance even when using a small sample of available data when compared to benchmark models.

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Long-term/mid-term electric load forecasting based on short-term correlation and annual growth

Cited by 166 publications

References 5 publications

Long Term Load Forecasting of Jimma Town for Sustainable Energy Supply

Long Term Load Forecasting of Jimma Town for Sustainable Energy Supply

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

Mid-Long Term Load Forecasting using Multi-Model Artificial Neural Networks

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