Short term load forecasting using hybrid adaptive fuzzy neural system: The performance evaluation

Minhas, Daud Mustafa; Khalid, Raja Rehan; Frey, Georg

doi:10.1109/powerafrica.2017.7991270

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…To address the gaps in the literature review, and to demonstrate a new practical approach for optimized power flow in a smart grid, we propose a scenario of HAPN with a hierarchical control framework as At the first stage, the optimization problem of obtaining cost-optimal scheduling signals for various ESEs is discussed. Originally, the parameters associated with the optimization algorithm are initiated and execute a forecasting algorithm [23] to predict the future load demands and electricity price information. The analytical models for PV source, storage, and HAPN architecture are proposed here.…”

Section: A Our Contributionmentioning

confidence: 99%

“…A linear programming based algorithm is used to operate the scheduling strategy that takes the duration of the whole day into account and analyses the future knowledge of the resources and the load demands. The optimal values of the control variables are estimated using mixed integer linear programming (MILP) that minimizes the objective function illustrated in (23). As a result, the statistical horizon shifts to the next period in time and the whole process is repeated.…”

Section: Multistage Power Scheduling and Sharing Control Frameworkmentioning

confidence: 99%

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Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Minhas

Frey

2021

IEEE Trans. on Ind. Applicat.

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An energy-efficient smart home generally integrates renewable energy sources (RESs) with intelligent energy devices (IEDs) to make itself cost-efficient. However, the stochastic nature of the RESs and IEDs makes the power network uncertain, and it degrades the economic efficiency of the smart home. Hence a co-simulation based intelligent home energy management system (HEMS) is proposed in this article. It interconnects the rolling horizon-based model predictive energy scheduling mechanism with robust control strategies. It manages the load following energy supply entities (ESEs) optimally and efficiently. A time-ahead scheduler integrates an optimal algorithm to estimate the cost-optimal (e.g., minimizing total energy cost) decision signals for various ESEs by incorporating component based losses and efficiencies. Besides, a real-time distributed robust control strategy is established executing a coordinated energy sharing mechanism by incorporating an auxiliary power source. Time-triggered low-jitter wireless communication architecture is adopted to transfer the optimal decision signals from the central scheduler to the distributed device level local controller. A scenario based comparison of various ac/dc models of a home area power network evaluates the strength of the prospective HEMS.

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Section: A Our Contributionmentioning

confidence: 99%

Section: Multistage Power Scheduling and Sharing Control Frameworkmentioning

confidence: 99%

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Minhas

Frey

2021

IEEE Trans. on Ind. Applicat.

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“…1 kWh of energy demands for any particular day as shown in the figure below. Similarly, We have incorporated a predictive PV energy model, that forecasts the exact PV power by putting in the predicted values of solar irradiance, ambient temperature, day of the week in (4) [51]. Hence, the total PV power output can be obtained by evaluating (1).…”

Section: A Prediction Modulementioning

confidence: 99%

Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Minhas

Frey

2020

IEEE Access

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Internet of energy based smart power grids demonstrate high in-feed from renewable energy resources (RESs) and lofty out-feed to energy consumers. Uncertainties evolved by incorporating RESs and time-varying energy consumption present immense challenges to the optimal control of smart power networks. To deal with these challenges, it is important to make the system deterministic by making timeahead prediction and scheduling of power supply and demand. The present work confers a model of a co-scheduling framework, organizing cost-efficient activation of energy supply entities (ESEs) and load demands in a home area power network (HAPN). It integrates roof-top photovoltaic (PV) panels, diesel energy generator (DE), energy storage devices (ESDs), and smart load demands (SLDs) along with gridsupplied power. The scheduling model is based on mixed-integer linear programming (MILP) framework, incorporates a ''min-max'' optimization algorithm that reduces the daily energy bills, maintains high comfort level for the energy consumers, and increases the self-sufficiency of the home. The proposed strategy exploits the flexibility in dynamic energy price signals and SLDs of various classes, providing day-ahead cost-optimal scheduling decisions for incorporated energy entities. A linearized component-based model is developed, considering inefficiencies, taking various power phase modes of the SLDs along with the cost of operation, maintenance, and degradation of the equipment. A case study based on numerical analysis determines the particular features of the proposed HAPN model. Simulation results demonstrate the real prospect of our implemented strategy, utilizing a cost-effective optimal blend of distinct energy entities in a smart home. c Minimum power usage by the TCL unit (W ). P TCL c Maximum power usage by the TCL unit (W ). T TCL c User's set maximum temperature ( • C). T TCL c User's set minimum temperature ( • C).

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“…Most of the targets of load forecasting model focus on the prediction accuracy. The tools were developed mainly based on artificial neural network (ANN) [8], fuzzy logic [9], Support Vector Machine (SVM) [10] and other time series forecasting models [11]. For instance, reference [12] designs an ANN-based model to ameliorate load forecasting accuracy in PJM market and ISO New England market [13].…”

Section: Introductionmentioning

confidence: 99%

Load Forecasting based on Deep Long Short-term Memory with Consideration of Costing Correlated Factor

Huang

Lai

et al. 2018

2018 IEEE 16th International Conference on Industrial Informatics (INDIN)

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In Day-ahead Power Market (DAM), Load Serving Entities (LSEs) needs to submit their load schedule to market operator beforehand. For reduction of the total cost, the disparity of the price of DAM and the price of RDM (Real Day Market) should be considered by the LSEs. Therefore, the problem is that a more accurate load-forecasting model sometimes provide a price that has an interspace will lead to a lower cost. Facing this issue, this paper initiates a load forecasting model considering the Costing Correlated Factor (CCF) with deep Long Short-term Memory (LSTM). The target of the forecast model contains both accuracy section and power cost section. At the same time, the construct of LSTM can offset the sacrificed accuracy. Also, this paper uses an Adaptive Moment Estimation algorithm for network training and the type of neuron is Rectified Linear Unit (ReLU). A numerical study based on practical data is presented and the result shows that LSTM with CCF can reduce energy cost with acceptable accuracy level.

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Short term load forecasting using hybrid adaptive fuzzy neural system: The performance evaluation

Cited by 12 publications

References 7 publications

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Load Forecasting based on Deep Long Short-term Memory with Consideration of Costing Correlated Factor

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