Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

Miyasawa, Ayumu; Akira, Shogo; Fujimoto, Yu; Hayashi, Yasuhiro

doi:10.1049/smc2.12011

Cited by 10 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other authors like McLoughlin et al (2015), Khan et al (2018) or Haben et al (2016 use smart meter data for characterizing residential customers according to their daily energy usage patterns utilizing clustering techniques. In Miyasawa et al (2021), the smart meter data of a major city is used for forecasting the spatial demand in the next hours.…”

Section: Related Workmentioning

confidence: 99%

A digital twin of a local energy system based on real smart meter data

Bayer

Pruckner

2023

Energy Inform

View full text Add to dashboard Cite

The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data.

show abstract

Section: Related Workmentioning

confidence: 99%

A digital twin of a local energy system based on real smart meter data

Bayer

Pruckner

2023

Energy Inform

View full text Add to dashboard Cite

show abstract

“…Conversely, information on power demand and generation for minutes or hours ahead predicted for each building helps to control power equipment, plan the operation of generated power, and optimise the energy use in all buildings, for energy management [20][21][22]. 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].…”

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%

“…The methodology consists of two steps; the first step derives the forecast result of the net demand of prosumers and pure‐consumers, which can be directly measured, and the second step derives the forecast results of the behind‐the‐metre pure demand and generation of prosumers, which cannot be directly measured. In the first step, we focussed here on a forecasting model that took into account daily periodicity, combining persistence and non‐parametric regression models, which had been proposed to achieve pure‐consumer demand forecasting [22]. We considered the impact on forecast accuracy when this forecast model was extended and applied to the net demand.…”

Section: Prediction Model For Pure Demand and Generation Based On Sma...mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…According to Miyasawa et al (2021), local energy self-sufficiency is achieved by combining the data reported by smart meters with distributed energy, which greatly reduces the operating difficulties of wide-area power grids. Aiming at the optimization of battery energy storage capacity, a charging and discharging strategy based on cost and time of use price was proposed, and an optimization model aimed at maximizing revenue was established.…”

Section: Distributed Energy Accessmentioning

confidence: 99%

Digital Twin Concept, Method and Technical Framework for Smart Meters

Irfan,

Niaz,

Habib

et al. 2023

ejtas

View full text Add to dashboard Cite

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected.

show abstract

Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

Cited by 10 publications

References 32 publications

A digital twin of a local energy system based on real smart meter data

A digital twin of a local energy system based on real smart meter data

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

Digital Twin Concept, Method and Technical Framework for Smart Meters

Contact Info

Product

Resources

About