From home energy management systems to communities energy managers: The use of an intelligent aggregator in a community in Algarve, Portugal

Gomes, I.L.R.; Ruano, M.G.; Ruano, A.E.

doi:10.1016/j.enbuild.2023.113588

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…Assume, without loss of generality, that our energy community has only two houses, whose loads are represented as Y (1) and Y (2) . We know that: (1) , Y (2) (52…”

Section: Community Using (47)mentioning

confidence: 99%

“…Energy communities made up of interconnected loads and distributed energy resources, can employ energysharing strategies that maximize community self-consumption and reduce dependence on the central grid. Different energy-sharing strategies are discussed in [1], where it is assumed that each community member has an intelligent home energy management system (HEMS) managing its own flow of energy under the supervision of an intelligent aggregator that controls the whole energy community and also responsible for the community's interaction with the central grid. Other approaches have been reported in the literature, such as the use of reinforcement learning for energy management of island groups [2].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

Ruano,

Ruano

2024

Energies

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The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained from a small energy community of four houses located in the southern region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

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“…Assume, without loss of generality, that our energy community has only two houses, whose loads are represented as Y (1) and Y (2) . We know that: (1) , Y (2) (52…”

Section: Community Using (47)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

Ruano,

Ruano

2024

Energies

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“…Very basically, HVAC is controlled so that it guarantees PMV thermal comfort within user-predefined schedules, while minimizing the energy consumed, making use of forecasting models of solar radiation, atmospheric air temperature and relative humidity, inside air temperature, relative humidity and mean radiant temperature, as well as room occupancy. Additionally, for a community of four houses, it allows to: Test and validate different control strategies for the community energy management system, which can be found in 63 , where the MILP-MBPC strategy described above is extended for a community of houses. Different ways to share the produced and stored energy are compared.…”

Section: Background and Summarymentioning

confidence: 99%

“…Test and validate different control strategies for the community energy management system, which can be found in 63 , where the MILP-MBPC strategy described above is extended for a community of houses. Different ways to share the produced and stored energy are compared.…”

Section: Background and Summarymentioning

confidence: 99%

From home energy management systems to energy communities: methods and data

Ruano,

Ruano

2024

Sci Data

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This paper introduces the HEMStoEC database, which contains data recorded in the course of two research projects, NILMforIHEM, and HEMS2IEA, for more than three years. To be manageable, the dataset is divided in months, from January 2020 until February 2023. It consists in: (a) consumption electric data for four houses in a neighbourhood situated in the south of Portugal, (b) weather data for that location, (c) photovoltaic and battery data, (d) inside climate data, and (e) operation of several electric devices in one of the four houses. Raw data, sampled at 1 sec and 1 minute are available from the different sensing devices, as well as synchronous data, with a common sampling interval of 5 minutes are available. Gaps existing within the data, as well as periods where interpolation was used, are available for each month of data.

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“…Energy communities, made up of interconnected loads and distributed energy resources (DERs), can employ energy management strategies that maximize community self-consumption and reduce dependence on the central grid. Different energy sharing strategies are discussed in [1], where it is assumed that each community member has an intelligent home energy management system (HEMS) managing its own flow of energy, under the supervision of an intelligent aggregator which controls the whole energy community and being also responsible for the community's interaction with the central grid.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

Ruano,

Ruano

2024

Preprint

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The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained by a small energy community of four houses located in the South region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

show abstract

From home energy management systems to communities energy managers: The use of an intelligent aggregator in a community in Algarve, Portugal

Cited by 5 publications

References 29 publications

A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

A Multi-Step Ensemble Approach for Energy Community Day-Ahead Net Load Point and Probabilistic Forecasting

From home energy management systems to energy communities: methods and data

A Multi-Step Ensemble Approach for Energy Community Day-ahead Net Load Point and Probabilistic Forecasting

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