A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework

Hernández, Leonardo; Baladrón, Carlos; Aguiar, Javier M.; Calavia, Lorena; Carro, Belén; Sánchez-Esguevillas, Antonio; Cook, Diane J.; Chinarro, David; Gómez, Jorge Marx

doi:10.3390/s120911571

Cited by 101 publications

(60 citation statements)

References 35 publications

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“…This makes sense, as seasons are roughly outlined, together with summer holidays (August) and Christmas (December). Electricity demand is seasonally dependent, as shown by Hernández et al [27]. 3.…”

Section: Classification With Self-organizing Mapmentioning

confidence: 97%

Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Hernández¹,

Baladrón

Aguiar

et al. 2012

Energies

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Understanding of energy consumption patterns is extremely important for optimization of resources and application of green trends. Traditionally, analyses were performed for large environments like regions and nations. However, with the advent of Smart Grids, the study of the behavior of smaller environments has become a necessity to allow a deeper micromanagement of the energy grid. This paper presents a data processing system to analyze energy consumption patterns in industrial parks, based on the cascade application of a Self-Organizing Map (SOM) and the clustering k-means algorithm. The system is validated with real load data from an industrial park in Spain. The validation results show that the system adequately finds different behavior patterns which are meaningful, and is capable of doing so without supervision, and without any prior knowledge about the data.

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Section: Classification With Self-organizing Mapmentioning

confidence: 97%

Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Hernández¹,

Baladrón

Aguiar

et al. 2012

Energies

Self Cite

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“…ANNs and WNNs include weather information, as it was shown that weather has a considerable Chapter 4. Implementation influence upon the energy consumption (Hernndez et al, 2012). The statistical methods rely on time-series regression in order to generate the prediction, and thus only use historical demand records.…”

Section: Fig 44: Variables Correlationmentioning

confidence: 99%

Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments

Marinescu

Dusparić

Clarke

2017

ACM Trans. Auton. Adapt. Syst.

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Multi-agent reinforcement learning (MARL) is a widely researched technique for decentralised control in complex large-scale autonomous systems. Such systems often operate in environments that are continuously evolving and where agents’ actions are non-deterministic, so called inherently non-stationary environments. When there are inconsistent results for agents acting on such an environment, learning and adapting is challenging. In this article, we propose P-MARL, an approach that integrates prediction and pattern change detection abilities into MARL and thus minimises the effect of non-stationarity in the environment. The environment is modelled as a time-series, with future estimates provided using prediction techniques. Learning is based on the predicted environment behaviour, with agents employing this knowledge to improve their performance in realtime. We illustrate P-MARL’s performance in a real-world smart grid scenario, where the environment is heavily influenced by non-stationary power demand patterns from residential consumers. We evaluate P-MARL in three different situations, where agents’ action decisions are independent, simultaneous, and sequential. Results show that all methods outperform traditional MARL, with sequential P-MARL achieving best results.

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“…It has been designed and implemented using the INGENIAS MAS methodology [14]. System agents are represented through roles which are reusable pieces of functionality.…”

Section: A Mas Oriented Framework For Vpp Controlmentioning

confidence: 99%

A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants

et al. 2013

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Recent technological advances in the Power Generation and Information Technologies areas are helping to change the modern electricity supply system, in order to comply with higher energy efficiency and sustainability standards. Smart Grids are an emerging trend which introduces intelligence in the power grid to optimize resource usage. In order for this intelligence to be effective, it is necessary to retrieve enough information about the grid operation together with other context data such as environmental variables and intelligently modify the behaviour of the network elements accordingly. This paper presents a Multi-Agent System model for Virtual Power Plants, a new power plant concept in which generation no longer occurs in big installations, but is the result of the cooperation of smaller and more intelligent elements. The proposed model is not only focused on the management of the different elements, but includes a set of agents which are embedded with Artificial Neural Networks for collaborative forecasting of disaggregated energy demand of domestic end users, the results of which are also shown in this paper.

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A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework

Cited by 101 publications

References 35 publications

Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Classification and Clustering of Electricity Demand Patterns in Industrial Parks

Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments

A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants

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