Reducing error propagation for long term energy forecasting using multivariate prediction

Many statistical and machine learning models for prediction make use of historical data as an input and produce single or small numbers of output values. To forecast over many timesteps, it is necessary to run the program recursively. This leads to a compounding of errors, which has adverse effects on accuracy for long forecast periods. In this paper, we show this can be mitigated through the addition of generating features which can have an “anchoring” effect on recurrent forecasts, limiting the amount of compounded error in the long term. This is studied experimentally on a benchmark energy dataset using two machine learning models LSTM and XGBoost. Prediction accuracy over differing forecast lengths is compared using the forecasting MAPE. It is found that for LSTM model the accuracy of short term energy forecasting by using a past energy consumption value as a feature is higher than the accuracy when not using past values as a feature. The opposite behavior takes place for the long term energy forecasting. For the XGBoost model, the accuracy for both short and long term energy forecasting is higher when not using past values as a feature.

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"Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

10.1101/2021.05.30.446339 ◽

2021 ◽

Author(s):

Yongmin Cho ◽

Rachael A Jonas-Closs ◽

Lev Y Yampolsky ◽

Marc W Kirschner ◽

Leonid Peshkin

Keyword(s):

Machine Learning ◽

Chronological Age ◽

Complex Behavior ◽

Learning Models ◽

Short Term ◽

Health Span ◽

Response Profile ◽

Automated Phenotyping ◽

Machine Learning Models

We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

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Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment

Energy ◽

10.1016/j.energy.2018.05.169 ◽

2018 ◽

Vol 158 ◽

pp. 17-32 ◽

Cited By ~ 37

Author(s):

Tanveer Ahmad ◽

Huanxin Chen ◽

Ronggeng Huang ◽

Guo Yabin ◽

Jiangyu Wang ◽

...

Keyword(s):

Machine Learning ◽

Learning Models ◽

Energy Prediction ◽

Term Energy ◽

Building Environment ◽

Machine Learning Models

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Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment

Energy ◽

10.1016/j.energy.2018.07.084 ◽

2018 ◽

Vol 160 ◽

pp. 1008-1020 ◽

Cited By ~ 26

Author(s):

Tanveer Ahmad ◽

Huanxin Chen

Keyword(s):

Machine Learning ◽

Smart Grid ◽

Energy Demand ◽

Learning Models ◽

Medium Term ◽

Grid Environment ◽

Term Energy ◽

Level Medium ◽

Machine Learning Models

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Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Applied Sciences ◽

10.3390/app11094266 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4266

Author(s):

Md. Shahriare Satu ◽

Koushik Chandra Howlader ◽

Mufti Mahmud ◽

M. Shamim Kaiser ◽

Sheikh Mohammad Shariful Islam ◽

...

Keyword(s):

Machine Learning ◽

Web Application ◽

Learning Models ◽

Short Term ◽

Infected People ◽

Infected Case ◽

First Case ◽

Novel Coronavirus ◽

Short Term Prediction ◽

Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

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Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽

10.3390/s21113678 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3678

Author(s):

Dongwon Lee ◽

Minji Choi ◽

Joohyun Lee

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Moving Average ◽

The Other ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Attention Model ◽

Long Short Term Memory ◽

Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

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Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII ◽

10.1117/12.2585829 ◽

2021 ◽

Author(s):

Patrick C. Riley ◽

Samir V. Deshpande ◽

Brian S. Ince ◽

Brian C. Hauck ◽

Kyle P. O'Donnell ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Ion Mobility ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Machine Learning Models

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Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

10.36227/techrxiv.16943395 ◽

2021 ◽

Author(s):

Navid Korhani ◽

Babak Taati ◽

Andrea Iaboni ◽

Andrea Sabo ◽

Sina Mehdizadeh ◽

...

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Fall Risk ◽

Learning Models ◽

Short Term ◽

Ambient Monitoring ◽

Clinical Assessments ◽

People With Dementia ◽

Time Of Admission ◽

Machine Learning Models

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.

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Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

E3S Web of Conferences ◽

10.1051/e3sconf/202021401023 ◽

2020 ◽

Vol 214 ◽

pp. 01023

Author(s):

Linan (Frank) Zhao

Keyword(s):

Machine Learning ◽

Age Groups ◽

Learning Models ◽

Public Authorities ◽

Ensemble Machine Learning ◽

European Public ◽

Data Driven Approach ◽

Using Data ◽

Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

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