Case study of Short Term Load Forecasting for weekends

Salim, Nur Ashida; Rahman, Titik Khawa Abdul; Jamaludin, Mohd Fadzil; Musa, Marsuddin

doi:10.1109/scored.2009.5443006

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…The ANN models developed during this research were subject to the aforementioned limitations; however, efforts were taken to mitigate these impediments. The training set met or exceeded the size of similar STLF ANN models [1,3,7,9,10] and contained a number of scenarios, both common and diverse with respect to weather conditions and load response.…”

Section: Limitations Of An Ann Modelmentioning

confidence: 99%

“…The benchmarking process considered a case study of the 2011 year across all hours and weekdays, which exceeded the evaluation events used in similar STLF ANN models [1,3,7,9] and utilized five statistical measures for model benchmarking, further described in section 6. Finally a systematic analysis of model optimization was enacted.…”

Section: Limitations Of An Ann Modelmentioning

confidence: 99%

“…It establishes the generation, capacity, and spinning reserve schedules which are posted to the market. Without optimal load forecasts, additional expenses due to uneconomic dispatch, over/under purchasing, and reliability uncertainty can cost a utility millions of dollars [1].…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches have been considered for STLF. The benefits of increased computational power and data storage have enhanced the capabilities of artificial intelligence methods for data analysis within the power industry [1]. Yet, even with the advancements of technology, industry forecasts are often based on a traditional similar day forecasting methodology or rigid statistical models with reduced variable modifiers for forecasting aggregated system load.…”

Section: Introductionmentioning

confidence: 99%

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Towards Developing a Decision Support System for Electricity Load Forecast

Wright¹,

Chan²,

Laforge³

2012

Decision Support Systems

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Short-term load forecasting STLF is an essential procedure for effective and efficient realtime operations planning and control of generation within a power system. It provides the basis for unit-commitment and power system planning procedures, maintenance scheduling, system security assessment, and trading schedules. It establishes the generation, capacity, and spinning reserve schedules which are posted to the market. Without optimal load forecasts, additional expenses due to uneconomic dispatch, over/under purchasing, and reliability uncertainty can cost a utility millions of dollars [ ].Many approaches have been considered for STLF. The benefits of increased computational power and data storage have enhanced the capabilities of artificial intelligence methods for data analysis within the power industry [ ]. Yet, even with the advancements of technology, industry forecasts are often based on a traditional similar day forecasting methodology or rigid statistical models with reduced variable modifiers for forecasting aggregated system load.Power systems with a large spatial presence provide an increased challenge for load forecasters, as they often face large diversity within their load centres as well as diverse weather conditions. These geographically separated load centres often behave independently and add considerable complexity to the system dynamics and forecasting procedure.This chapter investigates forecasting of electrical demand at an electric utility within the province of Saskatchewan, Canada and proposes a multi-region load forecasting system based on weather-related demand variables. The control area examined consists of over , kilometres of power lines with transmission voltages of , , and KV. The control area was apportioned into twelve load centres, consisting primarily of conforming loads. These conforming loads were cities and rural load clusters not including large indus-

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Section: Limitations Of An Ann Modelmentioning

confidence: 99%

Section: Limitations Of An Ann Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Developing a Decision Support System for Electricity Load Forecast

Wright¹,

Chan²,

Laforge³

2012

Decision Support Systems

View full text Add to dashboard Cite

show abstract

“…The research presented by [9] suggested Models based on the so-called Multi-Layer Perceptron (MLP) network to solve the problem of short term load forecasting. Finally, [10] presented a new method for STPLF to predict the demand in the future. The main objective of this study was to analyze the profile or pattern of the forecasted load and to predict the load demand during weekends.…”

Section: Introductionmentioning

confidence: 99%

Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid

Ibraheem¹,

Ali²

2014

IJCA

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This work presents proposed methodsfor short term power load forecasting (STPLF) for the governorate of Baghdad using two different models of Artificial Neural Networks (ANNs). The two models used in this work are the multi-layer perceptron (MLP) model trained with Levenberg-Marquardt Back Propagation (BP) algorithm and Radial Basis Function (RBF) neural network. Inputs to the ANN are thepast loadsvalues and the output of the ANN is the load forecast for the weekends of certain months for Baghdad governorate. The data is divided into two parts where half of them was used for training and the other half was used for testing the ANN. Simulations were achieved by MATLAB software with the aid of Neural networks toolbox, where the data obtained for the Iraqi national grid were rearranged and preprocessed. Finally, the simulations results showed that the forecasted load values for the Baghdad governorate by the proposed methods were very close to actual ones as compared with the traditional methods. General TermsLoad demand, neural networks, load prediction, artificial intelligence, energy consumption.

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