Algorithm selection and combining multiple learners for residential energy prediction

Güngör, Onat; Akşanlı, Barış; Aydoğan, Reyhan

doi:10.1016/j.future.2019.04.018

Cited by 31 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, combining multiple learners may improve the performance of the predictions as seen in [35,36]. As future work, we would like to introduce multiple learners for the underlying problem and combine them to obtain higher precision and recall.…”

Section: Discussionmentioning

confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru

Bouarfa²,

Arizar³

et al. 2020

Aerospace

Self Cite

View full text Add to dashboard Cite

Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

show abstract

Section: Discussionmentioning

confidence: 99%

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Doğru

Bouarfa²,

Arizar³

et al. 2020

Aerospace

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [5], the authors presented a method to manage energy demand-supply through hourly predictions of energy consumption based on historical data. The accurate prediction of energy demand allows providers to adjust the supply accordingly, thus, improving the efficiency of the energy production system.…”

Section: Background and Related Workmentioning

confidence: 99%

“…These works mostly follow an energy provider-centric perspective that does not fully benefit from the energy demand diversity among households and does not prioritise the customers' needs. An alternative household-centric perspective examines how to optimise the scheduling of electric appliances to avoid energy peak demands [5,6]. However, customers are often reluctant to any change of appliances' schedule that does not account for their preferences and specific needs.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Driven Decision Making and Planning for Energy Consumption

Fathy

Jaber

Nadeem

2021

JSAN

View full text Add to dashboard Cite

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

show abstract

“…Similarly, there has been a number of studies on the use of deep learning for forecasting load prediction for different energy consumer types -residential [47][48][49], commercial [50,51], and industrial [52,53]. While there are certainly learnings from these works, their focus is overwhelmingly on load forecasting for utility companies and grids.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the non-linear characteristics of the datasets used in this research, the need for both accuracy and fast run-times, and the promising results obtained in other works that used deep learning [47,49,62,69], three deep learning techniques were selected for STLF in this study -simple RNN, LSTM, and GRU. In addition to these techniques, three different machine learning techniques were selected for the purpose of comparison -SVR, Random Forest, and KNN.…”

Section: Finding Models To Predict Energy Consumptionmentioning

confidence: 99%

Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

Ribeiro¹,

Carmo²,

Rodrigues³

et al. 2020

Preprint

View full text Add to dashboard Cite

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

show abstract

Algorithm selection and combining multiple learners for residential energy prediction

Cited by 31 publications

References 34 publications

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Digital Twin-Driven Decision Making and Planning for Energy Consumption

Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

Contact Info

Product

Resources

About