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Short-Term Load Forecasting Using Artificial Neural Network in Indonesia Sylvia Jane Annatje Sumarauw(1) (1) Universitas Negeri Manado () Corresponding Author

Short-term Load Forecast (STLF) is a load forecasting that is very important to study because it determines the operating pattern of the electrical system. Forecasting errors, both positive and negative, result in considerable losses because operating cos... see more

Short-Term Load Forecast | Artificial Neural Network | Forecasting Performance | State Electricity Company

ILKOM Jurnal Ilmiah , 2023

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Hierarchical Clustering and Deep Learning for Short-Term Load Forecasting with Influenced Factors

Rio Indralaksono, M. Abdul Wakhid, Novemi Uki A, Galih Hendra Wibowo, M. Abdillah, Agus Budi Rahardjo, Diana Purwitasari

Stable and reliable electricity is one of the essential things that must be maintained by the transmission system operator (TSO). That can be achieved when the TSO is able to set the balance between demand and production. To maintain the balance between p... see more

forecasting | electricity | load | Long Short-term Memory | Analytical Hierarchical Clustering

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) , 2022

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ANN based Short-Term Load Curve Forecasting

Violeta Eugenia Chis,Constantin Barbulescu,Stefan Kilyeni,Simona Dzitac

A software tool developed in Matlab for short-term load forecasting (STLF) is presented. Different forecasting methods such as artificial neural networks, multiple linear regression, curve fitting have been integrated into a stand-alone application with a... see more

artificial neural networks | short-term load forecasting | articial intelligence | load curve

International Journal of Computers Communications & Control , 2018

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Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting

Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for short... see more

short-term load forecasting | deep learning | stacked denoising auto-encoder neural network

Energies , 2019

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Fisher Information Based Meteorological Factors Introduction and Features Selection for Short-Term Load Forecasting

Shuping Cai, Lin Liu, Huachen Sun and Jing Yan

Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature se... see more

short-term load forecasting | weather factors | feature selection | Fisher information

Entropy , 2018

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Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

Cruz E. Borges, Yoseba K. Penya, Iván Fernández, Juan Prieto and Oscar Bretos

Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its cons... see more

short term load forecasting | artificial intelligence | statistical methods

Energies , 2013

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A New Two-Stage Approach to Short Term Electrical Load Forecasting

Miloš Božic, Miloš Stojanovic, Zoran Stajic and Dragan Tasic

In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make... see more

short-term load forecasting | least-squares support vector machines | average daily load | two-stage approach

Energies , 2013

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A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Grey Relational Analysis

Dongxiao Niu and Shuyu Dai

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecastin... see more

short-term load forecasting | empirical mode decomposition | grey relational analysis | modified particle swarm optimization algorithm | least square support vector machine

Energies , 2017

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Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

Pan Duan, Kaigui Xie, Tingting Guo and Xiaogang Huang

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples use... see more

load forecasting | short-time load | PSO

Energies , 2011

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