SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm

Ghadimi, Noradin; Yasoubi, Elnazossadat; Akbari, Ehsan; Sabzalian, Mohammad Hosein; Alkhazaleh, Hamzah Ali; Ghadamyari, Mojtaba

doi:10.1016/j.heliyon.2023.e16827

Cited by 24 publications

(5 citation statements)

References 31 publications

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“…The overarching control logic plays a crucial role in orchestrating the entire system, serving as the core of the EMS. This orchestration ensures efficient and coordinated operation of the microgrid components to achieve optimal energy utilization [31]. In order to improve the optimization strategy, we employ smart EMS optimization formulation for improved efficiency.…”

Section: Energy Management System (Ems)mentioning

confidence: 99%

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Jamshidi,

Ghiasi,

Mehrandezh

et al. 2024

Smart Cities

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Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

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Section: Energy Management System (Ems)mentioning

confidence: 99%

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Jamshidi,

Ghiasi,

Mehrandezh

et al. 2024

Smart Cities

View full text Add to dashboard Cite

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“…The first layer of the network is designated as the input layer and contains neurons that receive input data [24]. The subsequent layers are comprised of 'hidden' neurons that extract features from the input data [25]. Finally, the output layer, which is the last layer of the network, is responsible for classifying input data or producing predictions [9].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Optimal load demand forecasting in air conditioning using deep belief networks optimized by an improved version of snake optimization algorithm

Jiang

Fathi

2023

IET Renewable Power Gen

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Air conditioning systems play a vital role in maintaining comfortable indoor environments, particularly in hot and humid climates. However, these systems consume a significant amount of energy, making load demand forecasting an important aspect of energy management. In this study, the authors propose a novel approach for load demand forecasting in air conditioning systems using a hybrid deep belief network (HDBN) and an improved snake optimization algorithm (ISOA). The HDBN is a machine learning technique that combines deep learning and probabilistic graphical models to capture complex patterns in the input data. The ISOA is a nature‐inspired optimization algorithm that mimics the movement of a snake to search for optimal solutions. The proposed approach is evaluated using real‐world data from a commercial building in a hot and humid region. The results show that the proposed HDBN/ISOA approach outperforms other commonly used techniques in terms of accuracy. The proposed approach can be used to optimize energy consumption and reduce costs in air conditioning systems.

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“…This section investigates the performance ability of the suggested Combined Group Teaching Optimization Algorithm in solving different optimization problems 49 , 50 . Here, the proposed method has been performed to 10 test functions collected from the CEC-BC-2017 test suite 51 .…”

Section: Combined Group Teaching Optimization Algorithmmentioning

confidence: 99%

Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm

Wang,

Liu,

2023

Sci Rep

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Oral cancer can occur in different parts of the mouth, including the lips, palate, gums, and inside the cheeks. If not treated in time, it can be life-threatening. Incidentally, using CAD-based diagnosis systems can be so helpful for early detection of this disease and curing it. In this study, a new deep learning-based methodology has been proposed for optimal oral cancer diagnosis from the images. In this method, after some preprocessing steps, a new deep belief network (DBN) has been proposed as the main part of the diagnosis system. The main contribution of the proposed DBN is its combination with a developed version of a metaheuristic technique, known as the Combined Group Teaching Optimization algorithm to provide an efficient system of diagnosis. The presented method is then implemented in the “Oral Cancer (Lips and Tongue) images dataset” and a comparison is done between the results and other methods, including ANN, Bayesian, CNN, GSO-NN, and End-to-End NN to show the efficacy of the techniques. The results showed that the DBN-CGTO method achieved a precision rate of 97.71%, sensitivity rate of 92.37%, the Matthews Correlation Coefficient of 94.65%, and 94.65% F1 score, which signifies its ability as the highest efficiency among the others to accurately classify positive samples while remaining the independent correct classification of negative samples.

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SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm

Cited by 24 publications

References 31 publications

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Optimal load demand forecasting in air conditioning using deep belief networks optimized by an improved version of snake optimization algorithm

Early diagnosis of oral cancer using a hybrid arrangement of deep belief networkand combined group teaching algorithm

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