Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern

Popoola, A. P. I.; Chipango, Moreblessing F

doi:10.1007/s12273-020-0601-x

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…The suggested solution outperforms existing options in experiments conducted on genuine 110 kV and 750 kV substations. Popoola et al (2021) [23] presented a method for controlling maximum demand based on the ranking of end-use appliances and event identification. The appliance that was selected by the customer was one of the most valuable components of the approach since it allowed inhabitants to adjust their load power to meet their demands at any time, regardless of whether generation capacity management was active or not.…”

Section: Deep Belief Network and Generalized Radial Basis Function Ne...mentioning

confidence: 99%

Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings

et al. 2022

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Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity.

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Section: Deep Belief Network and Generalized Radial Basis Function Ne...mentioning

confidence: 99%

Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings

et al. 2022

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“…Among these aspects, occupancy schedule and appliance use schedule are two essential inputs requiring data-driven analysis of the typical profiles. Pattern identification is a major approach to understanding the occupant-behavior-related energy profiles in buildings (Yang et al 2018a;Wen et al 2019;Quintana et al 2020;Popoola and Chipango 2020), and is widely applied in both residential and non-residential buildings. This provides insight for distinguishing different types of users or (Sala et al 2019;Escobar et al 2020).…”

Section: Parametric Analysis At the Design Phasementioning

confidence: 99%

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

et al. 2020

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Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

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“…Olawale Popoola et al provided an adaptive peak load control strategy for residential energy management that minimized peak demand and energy consumption [11]. In addition to addressing uncertainties in energy usage patterns, it resulted in significant reductions in peak demand (3% to 20%) and energy consumption (at least 14.05%) during time of use (ToU).…”

Section: Introductionmentioning

confidence: 99%

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Parangusam,

Lekshmana,

Gono

et al. 2023

Energies

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Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer’s power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelligent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm.

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Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern

Cited by 7 publications

References 27 publications

Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings

Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

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