Artificial Neural Networks Applied To Short Term Load Diagram Prediction

Hodžić, Nedim; Konjić, Tatjana; Miranda, Vladimiro

doi:10.1109/neurel.2006.341217

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When prediction is carried out over several periods, a series of error values are obtained for each time period [13], [14]. Mean square error (MSE) method is a better approach for examining the accuracy of prediction used in this study.…”

Section: Forecast Accuracymentioning

confidence: 99%

Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System

Pramelakumari¹

2018

IJCA

View full text Add to dashboard Cite

Demand forecasting is a procedure for identifying the electrical demand that can be expected from a specified number of consumers in a specified period. Electrical demand and electrical supply system could be in terms of average system demand, maximum system demand, and load demand in MW or energy demand in MWhr. Demand forecasting is usually undertaken with the prediction of hourly, daily, weekly and annually of the system demand and peak demand. Categorized form of load forecasting are short-term, longterm and medium-term which takes a few hours ahead of few weeks, one week to one year and five years to twenty years etc. This paper describes the demand forecasting of Kerala power system by using two different methods and a comparative analysis on its impact on the accuracy of load forecasting. This paper also present an error reduction based demand forecasting of Kerala power system. The performance evaluation parameters MAPE, MSE, RMSE, MAE/MAD and percentage error have been used for testing this proposed forecasting models with error reduction strategies.

show abstract

Section: Forecast Accuracymentioning

confidence: 99%

Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System

Pramelakumari¹

2018

IJCA

View full text Add to dashboard Cite

show abstract

“…ANN are known as a powerful method for handling nonlinear complex phenomenon, and are able to develop a forecasting model automatically only by training with stored actual data (Hodzic, 2006). Due to this capability, the authors implemented an ANN in Matlab software (MathWorks, 2010) that considers as inputs, the elements in (15).…”

Section: Ann-based Approachmentioning

confidence: 99%

ANN Based Support for Distributed Energy Resources Scheduling in Smart Grids

Vale

Morais

Faria

2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant.Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players' resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means. NOMENCLATURE

show abstract