Machine Learning Based Residential Energy Management System

N, Krishna Prakash; Vadana, D. Prasanna

doi:10.1109/iccic.2017.8524383

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…REMS technique has been proposed by Prakash et al [18] that efficiently changes conceivable lots to renewable thrilled native power storing centered based on charge-discharge dealings and grid obtainability thus plunging energy ingesting from the grid. Perera et al [19] proposed HOA technique by uniting Replacement and AEM in directive to haste up optimization procedure though upholding correctness. The efficiency of the solar panel used in the proposed technique can be calculated as follows:…”

Section: Realted Workmentioning

confidence: 99%

Renewable Energy Implementing Artificial Intelligence: Applications, Problems, and Challenges

Singh¹,

Vinoth²,

Singh³

et al. 2023

IJCATR

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World transformation has meaningfully abridged the chief gas, diesel, and coal power sources. Therefore, substitute power sources based on renewable energy mainly focus on fulfilling the energy demand of the world and avoiding global warming. Among different energy sources, solar energy is the critical substitute energy source for producing electricity using photovoltaic (PV). Conversely, energy engendering performance is highly dependent on cyclical and environmental factors. The changeable version of the environment shakes energy productivity and tends to create a disapproving influence on constancy, dependability, and the grid process. Therefore, a precise prediction of PV productivity is critically required to guarantee the endurance and reliability of the grid. The detailed study reviews the perilous techniques based on PV forecast using machine learning techniques. This paper summarizes different types of renewable energy with their merits and demerits. This paper also demonstrates the key challenges existing in renewable energy using machine learning and various artificial intelligence applications in real-life scenarios. Finally, an inclusive analysis of machine learning techniques in renewable energy through a detailed literature survey is presented to forecast energy production shortly better.

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Section: Realted Workmentioning

confidence: 99%

Renewable Energy Implementing Artificial Intelligence: Applications, Problems, and Challenges

Singh¹,

Vinoth²,

Singh³

et al. 2023

IJCATR

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show abstract

“…Enerjinin verimli kullanılması ve üretiminde çevreye verdiği zararın önlenmesi her bir bireyin bu konuda bilinçlenmesi ve sorumluluk alması ile mümkündür. Bu kapsamda, enerji yönetiminin evden baĢlaması gerektiği düĢünülerek konut enerji yönetim sistemi geliĢtirilmiĢtir [9]. Ġlgili çalıĢmada kontrol otomasyonu, optimize edilmiĢ insan benzeri kararlar önermek için yapay sinir ağları ve destek vektör makinesi kullanılarak gerçekleĢtirilmiĢtir.…”

Section: Wwwdergiparkgovtr/tdfdunclassified

Makine Öğrenmesi Yöntemleri Kullanılarak Elektrikli Cihazların Sınıflandırılması.

Koç

Karabiber

2021

Türk Doğa Ve Fen Dergisi

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Elektrik enerjisinin daha verimli bir Ģekilde kullanılabilmesi güç sistemlerinin takip ve analizi ile mümkündür. Üretim ve tüketim tarafında yer alan sistem bileĢenlerinin doğru tanımlanması durumunda, güç sistemlerinin karakteristik davranıĢları ve muhtemel tepkileri belirlenebilmektedir. Ayrıca, yük atma ve yük kaydırma gibi güç kontrol uygulamalarında elektrikli aletlerin yük modellerine göre sınıflandırılması gerekmektedir. Bu çalıĢmada, tüketim tarafında yer alan elektrikli cihazlar aktif ve reaktif güç tüketimleri referans alınarak sınıflandırılmaktadır. Örnek elektrikli cihaz olarak evlerde ve ofislerde sıkça kullanılan 5 adet farklı tüketim karakteristiğine sahip cihaz seçilmiĢtir. Sınıflandırma için son zamanlarda yaygın olarak kullanılan makine öğrenmesi algoritmalarından destek vektör makinaları, kolektif öğrenme ve karar ağaçları seçilip karĢılaĢtırılmıĢ ve bu sınıflandırıcıların yapılarından en iyi sonuçları veren beĢ algoritmanın performansı analiz edilmiĢtir. Elde edilen sonuçlara göre %83,4 ile en yüksek doğruluk oranını ve 50,285 saniye ile en kısa eğitim süresinde veren yöntem destek vektör makineleri olmuĢtur.

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“…This involves implementing an energy management algorithm, which considers the behaviour of the residential household load, as seen from the gridproducing/selling energy when prices are high, and consuming/buying energy when prices are low. The general idea has been investigated in the literature, in relation to other countries and cases [22][23][24][25][26][27]. This paper investigates BTMS with unchanged energy consumption behaviour in Denmark for residential consumers.…”

Section: The Flexible Pricing Schemementioning

confidence: 99%

Case Study of Residential PV Power and Battery Storage with the Danish Flexible Pricing Scheme

2019

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The economic viability of renewable energy generation is vital for sustainability. Ensuring that optimal operation is always achieved, using energy management systems and control algorithms, is essential in this endeavor. Here, a new real-time pricing scheme, the Danish flexible pricing scheme, illustrates how residential PV and battery systems can optimize the electricity bill of households, without changing consumption behavior or providing grid services in exchange. This means that the only addition is PV production, storage, and control. A case study is constructed from Danish household consumption data, irradiance measurements, and recorded spot prices. With the input data, the pricing scheme, and the energy flow, simulation models are computed in MATLAB, thereby validating the algorithmic potential and finding the best strategy for charging and discharging the energy storage unit. Different methods are compared to list the viable options and evaluate them, based on the economic feasibility for the household. Furthermore, a discussion of the system implementation is also included to highlight technical difficulties, co-integration opportunities, short-comings, and advantages present in the case study. In conclusion, it is possible to make renewable energy generation, and storage, viable for a Danish residential household under the new pricing scheme.

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Machine Learning Based Residential Energy Management System

Cited by 14 publications

References 7 publications

Renewable Energy Implementing Artificial Intelligence: Applications, Problems, and Challenges

Renewable Energy Implementing Artificial Intelligence: Applications, Problems, and Challenges

Makine Öğrenmesi Yöntemleri Kullanılarak Elektrikli Cihazların Sınıflandırılması.

Case Study of Residential PV Power and Battery Storage with the Danish Flexible Pricing Scheme

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