Forecasting electric demand of supply fan using data mining techniques

Cam, Mathieu Le; Daoud, Ahmed; Zmeureanu, Radu

doi:10.1016/j.energy.2016.02.061

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…Step Forecast Horizon Error [57] 5 min 40 min 13.2-14.4% (MAPE) Hourly [98] 15 min 1 h 4.5-5.4% (MAPE) [102] 5 min 1 h 8.59-23.86% (MAPE) Multiple hours [84] 1 h 1-6 h 7.30-8.48% (CV-RMSE) [64] 15 min 1-6 h 30% (CV-RMSE) Daily (profile) [78] 1 h 24 h 1.04-4.64% (MAPE) [85] 1 The overall performance range for the ANN models was found to be 0.001-36.5% (MAPE) for single step ahead forecasting. In contrast, multi-step ahead methods have shown increased errors with a performance range of 1.04-42.31% (MAPE).…”

Section: Sub-hourly Paper Id # Timementioning

confidence: 99%

“…In such models, ANN(s) are combined with the physics-based equations in order to leverage the advantages of both models and minimize the disadvantages of each. Le Cam et al applied a hybrid-ANN model in order to forecast the electric demand for a supply air fan of a commercial building [64]. The ANN model forecasted the supply fan modulation which was then sent to physics-based equations in order to estimate the electric demand for the supply fan.…”

Section: Limitations Of Using the Ann Forecasting Modelsmentioning

confidence: 99%

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Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

2019

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During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.

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Section: Sub-hourly Paper Id # Timementioning

confidence: 99%

Section: Limitations Of Using the Ann Forecasting Modelsmentioning

confidence: 99%

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

2019

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“…Most forecasting methods can be classified into two main methods namely causal and historical data based methods [12]. Causal methods mostly employ energy consumption as the output with some input variables such as economic, social and climate-related factors [12][13][14]. With causal-related methods, artificial neural networks and regression models are the methods frequently used for estimating energy demand.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, the accuracy of linear models depends on the choice of predictors as there are known and unknown predictors that may influence energy demand [27]. However, data own driven technique like the GRU dismisses the choice of predictors and further accounts for all unknown predictors that may cause volatilities in energy demand [13,23]. Although there are numerous advantages of employing deep learning techniques, most deep learning characteristics of GRU RNNs require massive datasets to help train the system rigorously [26].…”

Section: Introductionmentioning

confidence: 99%

Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

Ameyaw

Yao

2018

Sustainability

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In order to implement sustainable economic policies, realistic and high accuracy demand projections are key to drawing and implementing realizable environmentally-friendly energy policies. However, some core energy models projections depict considerably high forecast inaccuracies in their previous projections. The inaccuracies are due to the massive assumption-driven variables whose assumptions and scenarios typically deviate from their realized levels. Here, we propose a high-accuracy assumption-free own-data-driven technique that utilizes zero of the traditional determinants as well as assumptions or scenarios for sectorial energy demand forecasting; and implement it in the United States (U.S.). The results show that the forecast accuracy of our gated recurrent network presents an enormous improvement on Annual Energy Outlook 2008 forecast projections. With evidence that our proposed sequential algorithm outperformed Annual Energy Outlook 2008 forecast projections, our proposed algorithm will guide policymakers in making sustainable energy-related policies in the near future. Although future realized consumption levels are unknown, we present our estimated projections along with Annual Energy Outlook 2018 projections to inform policymakers on future energy demands for the commercial sector, industrial sector, residential sector, and transportation.

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“…In this context, as indicated in [19], techniques based on artificial intelligence are the most popular, including the use of expert systems, genetic algorithms and systems based on artificial neural networks. In particular, we find a good deal of available work in this area [20][21][22][23][24][25]. In [26], the authors design a neural network that is based on a supervised multilayer perceptron to predict time series of electricity consumption, taking as a case study the monthly electricity consumption data in Iran.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets

Martínez-Municio¹,

Rodríguez-Benítez²,

Castillo-Herrera³

et al. 2018

IJCIS

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Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and this can contribute to a building's sustainability. In this work, we propose a framework from which we can define models that capture this casuistry, gathering a set of time series of electrical consumption. The objective of these models is to obtain a linguistic summary based on y is P protoforms that describes in natural language the consumption of a given building or group of buildings in a specific time period. The definition of these descriptions has been solved by means of fuzzy linguistic summaries. As a novelty in this field, we propose an extension that is able to capture situations where the membership of the fuzzy sets is not very marked, which obtains an enriched semantics. In addition, to support these models, the development of a software prototype has been carried out and a small applied study of actual consumption data from an educational organization based on the conclusions that can be drawn from the techniques that we have described, demonstrating its capabilities in summarizing consumption situations. Finally, it is intended that this work will be useful to managers of buildings or organizational managers because it will enable them to better understand consumptionin a brief and concise manner, allowing them to save costs derived from energy supply by establishing sustainable policies.

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Forecasting electric demand of supply fan using data mining techniques

Cited by 25 publications

References 22 publications

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets

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