Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Izidio, Diogo M. F.; Neto, Paulo S. G. de Mattos; Barbosa, Luciano; Oliveira, João Fausto Lorenzato de; Marinho, Manoel H. N.; Rissi, Guilherme Ferretti

doi:10.3390/en14071794

Cited by 26 publications

(27 citation statements)

References 63 publications

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“…This application, typically used in transmission and distribution systems, aims to ensure that energy consumption at a given point is always within pre-established maximum and minimum limits, increasing the predictability of energy supply [21]. If consumption is lower than the established limit, the storage system recharges; if consumption is greater than the established limit, discharge is carried out [22].…”

Section: Load Levelingmentioning

confidence: 99%

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

Ramos

Pinheiro²,

Nascimento

et al. 2022

Sustainability

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With continuous technological advances, increasing competitiveness of renewable sources, and concerns about the environmental impacts of the energy matrix, the use of hybrid microgrids has been promoted. These generation microsystems, historically composed basically of fossil fuels as the main source, have experienced an energy revolution with the introduction of renewable and intermittent sources. However, with the introduction of these uncontrollable sources, the technical challenges to system stability, low diesel consumption, and security of supply increase. The main objective of this work is to develop an operation and control strategy for energy storage systems intended for application in hybrid microgrids with AC coupling. Throughout the work, a bibliographic review of the existing applications is carried out, as well as a proposal for modification and combination to create a new control strategy. This strategy, based on optimized indirect control of diesel generators, seeks to increase generation efficiency, reduce working time, and increase the introduction of renewable sources in the system. As a result, there is a significant reduction in diesel consumption, a decrease in the power output variance of the diesel generation system, and an increase in the average operating power, which ensures effective control of hybrid plants.

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Section: Load Levelingmentioning

confidence: 99%

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

Ramos

Pinheiro²,

Nascimento

et al. 2022

Sustainability

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“…The prediction of energy consumption in buildings using smart grids has been discussed in several studies [9][10][11][12]. One of the objectives of the smart grid is to optimize the energy performance, minimize environmental impacts, and control the influence of energy costs [13].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Optimized Hybrid System For Energy Consumption Forecasting From Smart Meters

Alves¹,

Oliveira²,

Madeiro³

et al. 2022

Learn. Nonlin. Mod.

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Smart grids are an alternative to minimize environmental impacts, such as CO2 emissions, and improve the efficiency of electricity consumption in buildings. Power grids enable adequate management and monitoring of consumption because of the periodic storage of measurements and easy access to them. In this scenario, an accurate prediction is a challenging task. Forecasting of consumption series is a defiant problem because data present linear and nonlinear patterns, and a dependence on external variables may be observed. Hybrid models are an alternative to mapping both patterns, which have been widely used to forecast load time series. Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models are used for this purpose, to map the linear and nonlinear patterns of the series, respectively. In this paper, a nonlinear optimized hybrid system based on ARIMA, SVR, and Particle Swarm Optimization (PSO) is proposed. The system can be divided into three steps. First, the linear patterns are predicted by the statistical model ARIMA. Then, the residual series is modeled using an optimized SVR, in which the parameters are selected from the PSO. One particularity from the proposal is to incorporate the choice of the topology and the inertia coefficient into the system. Lastly, the predictions are combined using the SVR. The simulations were conducted using a real database from smart meters of a building in Taiwan. To evaluate the performance of the proposed method, four related approaches were implemented and compared: a single ARIMA, two linear combination systems, and one non-linear combination system. The results show a superiority of the proposed method in terms of the metrics Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

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“…The final step performs the combinations of the forecasts from the time series and residual series. Sequential hybrid systems have been employed in several applications such as electricity [15], environmental sciences [16], etc.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Based Hybrid System for Residual Forecasting in Industrial Data

Vercosa¹,

Borba²,

Lima³

et al. 2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Time series forecasting has become an important task for several industrial processes. The employment of machine learning techniques also contributed to reduce costs and increase profits. In order to achieve accurate forecasts, it is important to perform a proper selection of parameters for the forecasting models. Hybrid sequential systems reduces model uncertainty through the employment of different models in the time series and residual series. In this work, an ensemble based hybrid sequential system is proposed, where a heterogeneous ensemble is used to perform residual forecasts. In this way, a pool of four models used in the industrial scenario are employed in order to achieve accurate forecasts. The experiments were conducted on real datasets from the industry in hourly averages and daily averages. The results show that the proposed system achieved promising results, and was able to improve the accuracy of the models in the pool in several cases.

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Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Cited by 26 publications

References 63 publications

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

A Nonlinear Optimized Hybrid System For Energy Consumption Forecasting From Smart Meters

An Ensemble Based Hybrid System for Residual Forecasting in Industrial Data

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