Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study

Jozi, Aria; Pinto, Tiago; Praça, Isabel; Ramos, Sérgio; Vale, Zita; Goujon, Bénédicte; Petrisor, Teodora

doi:10.1109/ssci.2017.8285200

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Table 5. Forecasted values for the electricity consumption of the TRT building (Jozi et al, 2017a) Hour 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 As it is visible in the Table 5, the electricity consumption of TRT building during 12 hours of an official day has less variation that the electricity consumption of the building N during a day. The standard deviation of the real electricity consumption of the TRT building during the presented hours is 80.43 W while this value for the building N is 530.69W.…”

Section: Resultsmentioning

confidence: 96%

“…In the (Jozi et al, 2017a) has been presented a study which also uses the same three used fuzzy rule based forecasting methods in order to predict the electricity consumption of the TRT building located in Palaiseau France. 12 hours of an official day (form 12h to 23h of 20/12/2013) has been chosen as the target hours in this work.…”

Section: Resultsmentioning

confidence: 99%

“…However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM; and also the results of (Jozi et al, 2017a), where several fuzzy rule based forecasting methods are used to predict the electricity consumption of an office building located in Palaiseau France. The case study is based on data referring to the electricity consumption of a campus building of ISEP/GECAD.…”

Section: Introductionmentioning

confidence: 99%

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Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

Jozi

Pinto

Praça

et al. 2019

ADCAIJ

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Electricity consumption; Forecasting; Fuzzy rule based methods; MOGUL This paper presents the application of a Methodology to Obtain Genetic fuzzy rulebased systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel's Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

Jozi

Pinto

Praça

et al. 2019

ADCAIJ

Self Cite

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“…Refs. [32,33] successfully applied fuzzy inference systems by mapping the relationship between electric consumption and variables affecting it. Although ANN is one of the popular techniques, it is unable to achieve lower error due to over fitting, issues in training, inappropriate generalization, and weakness of back propagation.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

et al. 2021

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With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

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“…Many fuzzy rule-based methods have been purposed to be used in various forecasting proposes. In [17], five fuzzy rule-based methods, namely as Wang and Mendel's method (WM), Hybrid neural Fuzzy Inference System (HyFIS), Genetic fuzzy systems for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL), Genetic lateral tuning and rule selection of linguistic fuzzy systems (GFS.LT.RS), and the simplified TSK fuzzy rule-generation method using heuristics and gradient descent method (FS.HGD), are used to predict the hourly energy consumption of TRT-France buildings located in Paris, France. This study shows that in this case, the GFS.FR.MOGUL presents the most trustable results, followed by HyFIS and WM.…”

Section: Introductionmentioning

confidence: 99%

Decision Support Application for Energy Consumption Forecasting

et al. 2019

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Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions (such as wind speed or solar intensity), the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and no method can outperform all others in all situations (no-free-lunch theorem). This paper proposes a novel application, developed in the scope of the SIMOCE project (ANI|P2020 17690), which brings together several of the most relevant forecasting methods in this domain, namely artificial neural networks, support vector machines, and several methods based on fuzzy rule-based systems, with the objective of providing decision support for energy consumption forecasting, regardless of the prediction conditions. For this, the application also includes several data management strategies that enable training of the forecasting methods depending on the available data. Results show that by this application, users are endowed with the means to automatically refine and train different forecasting methods for energy consumption prediction. These methods show different performance levels depending on the prediction conditions, hence, using the proposed approach, users always have access to the most adequate methods in each situation.

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Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study

Cited by 4 publications

References 19 publications

Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

Genetic fuzzy rule-based system using MOGUL learning methodology for energy consumption forecasting

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Decision Support Application for Energy Consumption Forecasting

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