Simulation of Household Appliances with Energy Disaggrigation using Deep Learning Technique

Nandish, B M; Pushparajesh, V.

doi:10.1109/compe53109.2021.9752375

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Manual detection of plant diseases is carried out through the naked eye, which is time consuming when taken on larger farms. Such manual detection some time provides error in prediction of the diseases (Nandish & Pushparajesh, 2021;Joshi & Jadhav, 2016). Due to the miss identification of the diseases, the yield of the plant decreases thereby causing loss to the farmers (Ramesh et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Detection using Deep Learning in Banana and Sunflower

H R,

G S,

et al. 2023

J. Adv. Zool.

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In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.

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Section: Introductionmentioning

confidence: 99%

Plant Disease Detection using Deep Learning in Banana and Sunflower

H R,

G S,

et al. 2023

J. Adv. Zool.

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“…The literature on customer energy consumption have mainly concentrated on responsive load models such as exponential, linear, and potential demand functions or demand elasticity-based methods [20]. The loads can be arranged based on various costs depending on the time of operation or tariff-based load variation [21]. In [22], a numerical experiments method has been proposed with synthetic data using demand models, including buildings, batteries, and aggregations of price-responsive loads.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

et al. 2022

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This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid.

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Efficient power management based on adaptive whale optimization technique for residential load

Nandish,

Pushparajesh

2024

Electr Eng

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Simulation of Household Appliances with Energy Disaggrigation using Deep Learning Technique

Cited by 4 publications

References 38 publications

Plant Disease Detection using Deep Learning in Banana and Sunflower

Plant Disease Detection using Deep Learning in Banana and Sunflower

Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings

Efficient power management based on adaptive whale optimization technique for residential load

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