PowerNet: a smart energy forecasting architecture based on neural networks

Cheng, Yong; Xu, Chang; Mashima, Daisuke; Biswas, Partha Pratim; Chipurupalli, Geetanjali; Zhou, Bin; Wu, Yongdong

doi:10.1049/iet-smc.2020.0003

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…In [89], it is stated that in order to have an appropriate electric transmission, an anomaly detection system is required to avoid power losses. In this article, an AI method called PowerNet, which is based on neural networks, is proposed to detect anomalies for electricity theft detection in the smart grids, so the AI could make a contribution to automatically detect these problems and report them.…”

Section: Energy Transmissionmentioning

confidence: 99%

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Camacho,

Aguirre,

Ponce

et al. 2024

Energies

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As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.

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Section: Energy Transmissionmentioning

confidence: 99%

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Camacho,

Aguirre,

Ponce

et al. 2024

Energies

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“…For efficient energy management and a stable power supply through a power grid, hourly or perminute power grid forecast results play key roles, because consideration of the time-series balance between generation and demand is necessary [21]. For example, Cheng et al [23] have proposed a neural network architecture which can…”

Section: Literature Reviewmentioning

confidence: 99%

“…Short term ahead (hourly or per minute) One or some buildings Energy management in houses or buildings [20] A city Regional energy management for local self-sufficiency (Target of this study) [21,22] Power grid Stabilisation of the power system [23] Long term ahead (monthly or yearly) One or some buildings Determination of equipment configuration [24,25] A city Estimation potential of additional PV installation [16,19] Power grid Investment decisions in power grid and generation facilities [26][27][28] incorporate multiple heterogeneous features from a smart grid that exploits sensing and information communication technologies for demand forecasting. Conventionally, demand data in cities were measured monthly for billing by power companies, and no mechanism for collecting the spatio-temporal power demand existed; therefore, they have proposed the installation of additional sensors in the power grid.…”

Section: Relevant Workmentioning

confidence: 99%

“…We stress that these relevant forecasting methods are difficult to contribute to operations to improve local energy selfsufficiency in cities without advanced grid sensing, as they are based on some buildings rather than area scale [29,30], based on the long-term demand forecasting [26][27][28], not consider behind-the-metre pure demand and generation [21], or assume the installation of additional metres on the power grid [23].…”

Section: Study Gapsmentioning

confidence: 99%

“…For example, Cheng et al. [23] have proposed a neural network architecture which can incorporate multiple heterogeneous features from a smart grid that exploits sensing and information communication technologies for demand forecasting. Conventionally, demand data in cities were measured monthly for billing by power companies, and no mechanism for collecting the spatio‐temporal power demand existed; therefore, they have proposed the installation of additional sensors in the power grid.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

et al. 2023

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Local energy self‐sufficiency, in which the supply and demand of electricity are controlled such that the generated power from distributed energy resources (DERs) is consumed locally based on a power supply‐and‐demand forecast, mitigates the burden on the power system and contributes to the efficient use of DERs in smart cities. However, widely available smart metres cannot measure behind‐the‐metre pure demand and generation from prosumers. Pure demand and generation forecasts without additional metering contribute to advanced supply‐and‐demand control in smart cities, including demand response. This study proposes a method of forecasting spatio‐temporal behaviours of behind‐the‐metre pure demand and generation by focussing on the information of net demand distribution observable from the smart metres; the proposed method initially predicts the spatio‐temporal net demand distribution with a combined forecaster based on the persistence and non‐parametric regression models, and then separately estimates the behind‐the‐metre pure demand and generation by using demand forecast result of neighbouring pure‐consumers extracted by considering the area‐scale behaviours of the smart metering data. The simulation results demonstrate that the proposed method provides accuracy comparable to forecasts conducted by directly measuring pure demand and generation, without requiring the installation of additional metres.

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DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings

Mishra,

Lone,

Mishra

2024

Energy and Buildings

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PowerNet: a smart energy forecasting architecture based on neural networks

Cited by 6 publications

References 44 publications

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review

Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings

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