Short-term Load Forecasting in Grid-connected Microgrid

Izzatillaev, J; Yusupov, Zıyodulla

doi:10.1109/sgcf.2019.8782424

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…These resources can be inconsistent due to the fact that they rely on stochastic boundaries, such as solar irradiance, climate, and wind speed [66]. On the other hand, energy demand forecasting is needed to make microgrids possible and proficient in their determination of likely electric demand [67]. Forecasting processes can be carried out at different time intervals, such as very short-term (seconds to hours), short-term (hours to one week), medium-term (weeks to a month), and long-term (months to a year) [68].…”

Section: Generation and Demand Forecasting Approachesmentioning

confidence: 99%

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

et al. 2021

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It is expected that distribution power systems will soon be able to connect a variety of microgrids from residential, commercial, and industrial users, and thus integrate a variety of distributed generation technologies, mainly renewable energy sources to supply their demands. Indeed, some authors affirm that distribution networks will propose significant changes as a consequence of this massive integration of microgrids at the distribution level. Under this scenario, the control of distributed generation inverters, demand management systems, renewable resource forecasting, and demand predictions will allow better integration of such microgrid clusters to decongest power systems. This paper presents a review of microgrids connected at distribution networks and the solutions that facilitate their integration into such distribution network level, such as demand management systems, renewable resource forecasting, and demand predictions. Recent contributions focused on the application of microgrids in Low-Voltage distribution networks are also analyzed and reviewed in detail. In addition, this paper provides a critical review of the most relevant challenges currently facing electrical distribution networks, with an explicit focus on the massive interconnection of electrical microgrids and the future with relevant renewable energy source integration.

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Section: Generation and Demand Forecasting Approachesmentioning

confidence: 99%

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

et al. 2021

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“…For South Korea's hourly load data, Yu et al suggested a forecasting methodology based on SVR, which implements GMDH networks and bootstrap methods for the input selection procedure in comparison with different variations of linear correlation (LC) and mutual information (MI) based filter methods [39]. Izzatillaev and Yusupov analyzed hourly electrical energy consumption forecasting in a grid-connected microgrid within a commercial bank by employing GMDH networks and ANN [40].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

et al. 2020

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Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

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“…Several techniques are commonly used to forecast dayahead load in the microgrid such as time series analysis (TSA) [27]; decision trees and random forests (DTRF) [28]; gradient boosting model (GBM) [29]; neural network (NN) with training algorithms of Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient [30]- [31]; support vector regression (SVR) [32]; convolutional neural network (CNN) [33]; long short-term memory (LSTM) [34]; Kalman filtering (KF) [35]; and linear regression (LR) [36].…”

Section: Introductionmentioning

confidence: 99%

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

Huynh,

Ho,

Pham

et al. 2024

IEEE Access

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A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response to dynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-ahead load forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of population size, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism. It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is applied to the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solved by day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs, ensuring optimal decision-making in the face of varying load scenarios. With the competition of various dayahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows its advantage features, being more transparent, more effective, and more robust than other techniques, especially transparent explainability, as well as simple and fast in model training. These are requirements to achieve the result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load in the microgrid in this paper. The simulation results show that the percentage error (PE) between the MLR modelbased forecasted and actual load powers is always less than 4.42%, the mean absolute percentage error (MAPE) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievements meet the accurate and fast requirements. They are completely competitive with the results of using other techniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especially in the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch in the microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are 68.76 ($), 5.09 ($), and 73.85 ($) respectively by using the WWO algorithm which are better than those by using a genetic algorithm (GA), a non-dominated sorting genetic algorithm-II (NSGA-II), a particle swarm optimization (PSO) algorithm, and a transient search optimization (TSO) algorithm in the microgrid. The findings offer valuable insights for microgrid operators, energy planners, and policymakers seeking sustainable and cost-effective solutions for distributed energy resource management.INDEX TERMS Dynamic optimal dispatch, day-ahead load forecasting, microgrid, water wave optimization algorithm

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Short-term Load Forecasting in Grid-connected Microgrid

Cited by 13 publications

References 12 publications

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

A Review of Low-Voltage Renewable Microgrids: Generation Forecasting and Demand-Side Management Strategies

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

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