A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

Atef, Sara; Ismail, Nourhan; Eltawil, Amr B.

doi:10.1080/17512549.2021.1873183

Cited by 18 publications

(6 citation statements)

References 32 publications

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“…The electrical load may be split into two sections in a hybrid model: a scaled curve load with five Artificial Neural Networks (ANNs) and day maxima and minima with fuzzy logic and ANNs for LF. The suggested hybrid structure had the advantage of using fuzzy logic and ANNs to handle uncertainty [ 21 ]. The noise in data and misspecification are distinctly identified.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble learning approach for advanced metering infrastructure in future smart grids

Irfan,

Ayub,

Althobiani

et al. 2023

PLoS ONE

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Typically, load forecasting models are trained in an offline setting and then used to generate predictions in an online setting. However, this approach, known as batch learning, is limited in its ability to integrate new load information that becomes available in real-time. On the other hand, online learning methods enable load forecasting models to adapt efficiently to new incoming data. Electricity Load and Price Forecasting (ELPF) is critical to maintaining energy grid stability in smart grids. Existing forecasting methods cannot handle the available large amount of data, which are limited by different issues like non-linearity, un-adjusted high variance and high dimensions. A compact and improved algorithm is needed to synchronize with the diverse procedure in ELPF. Our model ELPF framework comprises high/low consumer data separation, handling missing and unstandardized data and preprocessing method, which includes selecting relevant features and removing redundant features. Finally, it implements the ELPF using an improved method Residual Network (ResNet-152) and the machine-improved Support Vector Machine (SVM) based forecasting engine to forecast the ELP accurately. We proposed two main distinct mechanisms, regularization, base learner selection and hyperparameter tuning, to improve the performance of the existing version of ResNet-152 and SVM. Furthermore, it reduces the time complexity and the overfitting model issue to handle more complex consumer data. Furthermore, numerous structures of ResNet-152 and SVM are also explored to improve the regularization function, base learners and compatible selection of the parameter values with respect to fitting capabilities for the final forecasting. Simulated results from the real-world load and price data confirm that the proposed method outperforms 8% of the existing schemes in performance measures and can also be used in industry-based applications.

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Section: Literature Reviewmentioning

confidence: 99%

Ensemble learning approach for advanced metering infrastructure in future smart grids

Irfan,

Ayub,

Althobiani

et al. 2023

PLoS ONE

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“…Although fuzzy logic usage in the container stacking problem is also scarce, it showed that its implementation provided an efficient decision tool in a wide variety of problems. Furthermore, fuzzy logic showed high-quality results in many applications; scheduling [51], sequencing [52], and routing [53] among others. Therefore, fuzzy logic is adopted here.…”

Section: The Slot Planning Phasementioning

confidence: 99%

A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels

2021

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Background: The slot planning problem is a container allocation problem within a certain location on a vessel. It is considered a sub-problem of a successful decomposition approach for the container vessel stowage planning problem. This decision has a direct effect on container handling operations and the vessel berthing time, which are key indicators for the container terminal efficiency. Methods: In this paper, an approach combining a rule-based fuzzy logic algorithm with a rule-based search algorithm is developed to solve the slot planning problem. The rules in the proposed fuzzy logic algorithm aim at improving the objective function and minimizing/eliminating constraint violation. Results: The computational results of 236 slot planning instances illustrate the efficiency and effectiveness of the proposed algorithm. Conclusions: The results show that the proposed approach is fast and can produce optimal or near-optimal solutions for a comprehensive industrial set of instances.

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“…A wide range of techniques is already available in the literature and these techniques can be classified into two main categories [14][15][16][17][18]. Indeed, these approaches are based either on information processing methods or on optimization tactics:…”

Section: Introductionmentioning

confidence: 99%

A Bidirectional Grid-Connected DC–AC Converter for Autonomous and Intelligent Electricity Storage in the Residential Sector

et al. 2022

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Controlling the cost of electricity consumption remains a major concern, particularly in the residential sector. Smart home electricity management systems (HEMS) are becoming increasingly popular for providing uninterrupted power and improved power quality, as well as for reducing the cost of electricity consumption. When power transfer is required between a storage system and the AC grid, and vice versa, these HEMS require the use of a bidirectional DC–AC converter. This paper emphasizes the potential value of an almost unexplored topology, the design of which was based on the generation of sinusoidal signals from sinusoidal half waves. A DC–DC stage, which behaved as a configurable voltage source, was in series with a DC–AC stage, i.e., an H-bridge, to achieve an architecture that could operate in both grid and off-grid configurations. Wide bandgap power switches (silicon carbide metal-oxide-semiconductor field-effect transistors [MOSFETs]), combined with appropriate control strategies, were the keys to increasing compactness of the converter while ensuring good performance, especially in terms of efficiency. The converter was configured to automatically change the operating mode, i.e., inverter or rectifier in power factor correction mode, according to an instruction issued by the HEMS; the latter being integrated in the control circuit with automatic duty cycle management. Therefore, the HEMS set the amount of energy to be injected into the grid or to be stored. The experimental results validate the operating modes of the proposed converter and demonstrate the relevance of such a topology when combined with an HEMS, especially in the case of an AC grid connection. The efficiency measurements of the bidirectional DC–AC converter, performed in grid-connected inverter mode, show that we exceeded the efficiency target of 95% over the entire output power range studied, i.e., from 100 W to 1.5 kW.

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A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

Cited by 18 publications

References 32 publications

Ensemble learning approach for advanced metering infrastructure in future smart grids

Ensemble learning approach for advanced metering infrastructure in future smart grids

A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels

A Bidirectional Grid-Connected DC–AC Converter for Autonomous and Intelligent Electricity Storage in the Residential Sector

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