Models for mid-term electricity demand forecasting incorporating weather influences

Mirasgedis, S.; Sarafidis, Y.; Georgopoulou, E.; Lalas, D. P.; Moschovits, M.; Karagiannis, F.E.; Papakonstantinou, D.

doi:10.1016/j.energy.2005.02.016

Cited by 252 publications

(131 citation statements)

References 19 publications

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“…[71] proposed a MTLF methodology for 1-year period with monthly load demand based on time series and statistical approach. The method was tested for the Greek power system.…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

“…[68][69][70] modelled MTLF in monthly forecast step, and refs. [21,[71][72] considered a horizon of 12 months or 1 year for their study. Many of the MTLF modelling methods were discussed in each of the sections discussing on various approaches for MTLF/LTLF.…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

“…[77,[79][80][81]. AI-based methods have been widely used for MTLF, mostly ANN [30,32,71,78,[82][83][84].…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

135

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…[71] proposed a MTLF methodology for 1-year period with monthly load demand based on time series and statistical approach. The method was tested for the Greek power system.…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

135

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“…Statistics method is suitable for linear type of data. for example humidity, heat or temperature or meteorological parameter and historical monthly load [51].…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

“…6) Statistics [51][52][53] proposes the methods of load forecasting by using the principle of mathematic or statistic such as Physical series algorithm [52] Autoregressive [51] and Nonlinear regression [53]. Statistics method is suitable for linear type of data.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

Bunnoon¹,

Chalermyanont²,

Limsakul³

2010

IJET

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Abstract-This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).

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Weather impacts on electric power load: partial phase synchronization analysis

Hong

Kim

2015

Meteorological Applications

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Electricity demand is influenced by atmospheric conditions, and, therefore it is important to quantify their relationships suitably for accurate electricity demand forecasting and the implementation of power-saving policies. However, interdependencies and characteristics of covariance among meteorological variables within the same periodicities hinder the quantification of their direct and indirect impacts on electric power load. To investigate the strength of the direct correlation between atmospheric conditions and electric power load, this study harnessed a new partialization analysis method based on a partial phase synchronization index combined with wavelet transformation. The advantage of the proposed method is that it can be used to evaluate the degree of independent contribution of the variables over different spatiotemporal scales. Compared with traditional statistical analyses, this new partialization analysis shows that air temperature is the principal variable associated directly with electricity demand, but that the strength of the relationship varies with season and time scale. Relative humidity and wind speed have strong direct correlations with electricity in summer and winter, respectively. Insolation is directly coupled to the electric power load only on sub-diurnal time scales. This investigation indicates that for accurate forecasting of electricity demand, changes in the coupling strengths of different atmospheric variables should be incorporated into the electric power load forecasting process. The study shows that a partial phase synchronization index, combined with wavelet transformation, is a useful tool that could be used in other studies to assess complex interacting atmospheric oscillations that cannot be assessed properly by traditional approaches.

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Models for mid-term electricity demand forecasting incorporating weather influences

Cited by 252 publications

References 19 publications

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

Weather impacts on electric power load: partial phase synchronization analysis

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