A Review of Short Term Load Forecasting using Artificial Neural Network Models

Baliyan, Arjun; Gaurav, Kumar; Mishra, Sumit

doi:10.1016/j.procs.2015.04.160

Cited by 192 publications

(83 citation statements)

References 21 publications

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“…There are several types of methods that have been applied to find the best forecasting model [3]. Recent studies have shown that load consumption patterns are highly related to exogenous factors such as weather conditions,…”

Section: Introductionmentioning

confidence: 99%

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

et al. 2018

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Abstract:The estimation of hourly electricity load consumption is highly important for planning short-term supply-demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base-which is defined by expert insights and decisions-gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA) techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE) of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014). The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment.

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Section: Introductionmentioning

confidence: 99%

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

et al. 2018

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“…Various machine learning techniques such as neural networks (NN) have attracted greater attention for applications in DSM. Often, their application can be found in load forecasting [23,24]. Furthermore, various types of home energy management systems implement machine learning for decision making.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management

2018

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Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent constraints. To overcome these problems, this paper presents a time-horizon three-phase grid-supportive demand side management methodology for low voltage networks by using a universal interface that is established between the demand side management application and the monitoring and network analysis tools of the network operator. Using time-horizon predictions of the system states that the probability of operational limit violations is identified. Since this analysis is computationally intensive, a data driven approach is adopted by using machine learning. Time-horizon flexibility is procured, which effectively prevents operation limit violation from occurring independent of the objective that the demand side management application has. A practical example featuring fair power sharing demonstrates the effectiveness of the presented method for resolving over-voltages and under-voltages. This is followed by conclusions and recommendations for future work.

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“…Genel anlamda geliştirilen metotlar, ülkelerin iklimsel ve enerji kaynakları açısından farklı özelliklerini ve toplum kültürlerinden gelen davranış biçimlerini değerlendirmekten uzak olduğundan, bunların her ülkeye uygulanması mümkün değildir. Bu [9], çalışmalarında kısa dönem elektrik tahmininde kullanılan farklı hibrit YSA modellerini incelemiş ve doğrusal olmayan zaman serilerinin kullanılması sırasında karşılaşılan problemlerin yapay zekâ tabanlı tahmin programları ile üstesinden gelinebileceğini belirlemiştir. Baziar [10], İran'ın kısa dönem elektrik yük değerlerini tahmin etmek için oluşturulan YSA'lardaki eksikliklerin üstesinden gelebilecek destek vektör regresyon analizine dayalı yeni bir hibrit yöntem önermiştir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Kısa Dönem Elektrik Talep Tahminleri İçin Yapay Sinir Ağları ve Uzman Sistemler Tabanlı Hibrid Tahmin Sistemi Geliştirilmesi

Bulut¹,

Başoğlu²

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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A Review of Short Term Load Forecasting using Artificial Neural Network Models

Cited by 192 publications

References 21 publications

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management

Kısa Dönem Elektrik Talep Tahminleri İçin Yapay Sinir Ağları ve Uzman Sistemler Tabanlı Hibrid Tahmin Sistemi Geliştirilmesi

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