Prediction Improvement via Smooth Component Analysis and Neural Network Mixing

Szupiluk, Ryszard; Wojewnik, Piotr; Ząbkowski, Tomasz

doi:10.1007/11840930_14

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…Different methods have been proposed for forecasting the electric load demand in the last decades. Some of the most popular include time series analyses with the autoregressive integrated moving average (ARIMA) method [12], fuzzy logic [13], the neuro-fuzzy method [14], artificial neural networks (ANNs) [15–17], and support vector machines (SVMs) [18–19]. …”

Section: Literature Review Of Similar Problemsmentioning

confidence: 99%

Electricity forecasting on the individual household level enhanced based on activity patterns

Gajowniczek

Ząbkowski

2017

PLoS ONE

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Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

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Section: Literature Review Of Similar Problemsmentioning

confidence: 99%

Electricity forecasting on the individual household level enhanced based on activity patterns

Gajowniczek

Ząbkowski

2017

PLoS ONE

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“…Moreover, a respective component might have a constructive effect on one model and a destructive effect on another. There might also be components that are destructive when they are single but constructive when considered in a pair or in a group [45]. This means that it might exists a better mixing system than the one described by A, that uses simple linear relationship.…”

Section: Neural Mixing Systemmentioning

confidence: 99%

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

et al. 2020

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In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

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“…ANNs, with their high modeling capability and their ability to generalize non-linear relations, gained widespread popularity for general forecasting in a variety of business, industry, and science applications [30,31]. Neural network models have been extensively studied and successfully applied to short-term electricity forecasting [32][33][34][35]. Some researchers have worked on ANN models applicable to medium-and long-term forecasting [36,37].…”

Section: Literature Review On Related Problemsmentioning

confidence: 99%

Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

Gajowniczek

Ząbkowski

2017

Energies

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Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution) and deliver accurate forecasts, with mean absolute percentage error (MAPE) of 3.10% and resistant mean absolute percentage error (r-MAPE) of 2.70% for the 24 h forecasting horizon.

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Prediction Improvement via Smooth Component Analysis and Neural Network Mixing

Cited by 12 publications

References 15 publications

Electricity forecasting on the individual household level enhanced based on activity patterns

Electricity forecasting on the individual household level enhanced based on activity patterns

Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

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