Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

Neto, Paulo S. G. de Mattos; Oliveira, João Fausto Lorenzato de; Bassetto, Priscilla; Siqueira, Hugo Valadares; Barbosa, Luciano; Alves, Emilly Pereira; Marinho, Manoel H. N.; Rissi, Guilherme Ferretti; Fu, Li

doi:10.3390/s21238096

Cited by 13 publications

(5 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Demand response, by managing energy demand in real-time and balancing the energy supply and demand [15][16][17] , reducing the need for expensive peak generation. • Power system planning, by making predictions about future energy demand [18][19][20] , helping energy companies to plan for the future and make informed decisions about investments in generation and transmission. • Design of policies and programs to encourage energy conservation and promote the use of renewable energy sources, by understanding human behavior related to energy consumption 11,21,22 .…”

Section: Background and Summarymentioning

confidence: 99%

An electricity smart meter dataset of Spanish households: insights into consumption patterns

Quesada,

Astigarraga,

Merveille

et al. 2024

Sci Data

View full text Add to dashboard Cite

Smart meters are devices that provide detailed information about the energy consumed by specific electricity supply points, such as homes, offices, and businesses. Data from smart meters are useful for modeling energy systems, predicting electricity consumption, and understanding human behavior. We present the first smart meter dataset from Spanish electricity supply points, expanding the geographic diversity of available data on energy consumption at the household level and reducing biases in existing data, which typically come from a limited number of countries. The dataset consists of 25,559 raw hourly time series with an average length of nearly three years, spanning from November 2014 to June 2022. It also includes three subsets obtained by segmenting and cleaning the raw time series data, each focusing on the periods before, during, and after the COVID-19 lockdowns in Spain. This dataset is a valuable resource for studying electricity consumption patterns and behaviors that emerge in response to different natural experiments, such as nationwide and regional lockdowns, nighttime curfews, and changes in electricity pricing.

show abstract

Section: Background and Summarymentioning

confidence: 99%

An electricity smart meter dataset of Spanish households: insights into consumption patterns

Quesada,

Astigarraga,

Merveille

et al. 2024

Sci Data

View full text Add to dashboard Cite

show abstract

“…They find that the hybrid model employed generates a superior forecast compared to ARIMA, SVR and other variants. Neto et al 33 proffer a data-driven ensemble that combines five forecast methods by employing extreme learning machines (ELM) as the combination model. They find that the ELM-based ensemble is superior to the single forecast approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Bala¹,

Shuaibu

2022

Energy & Environment

View full text Add to dashboard Cite

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector.

show abstract

“…ANNs have better superiority in assessing electrocardiogram (ECG) data [19], while SVMs are in disease stratification [20]. However, ANNs and SVMs could not dispose all conditions equally because of over-fitting, underfitting, and misspecification [21]. Deep learning has relatively good performance in processing image data, and current deep learning algorithms in cardiovascular medicine include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep neural networks (DNNs) [22].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

et al. 2023

View full text Add to dashboard Cite

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

show abstract

Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

Cited by 13 publications

References 65 publications

An electricity smart meter dataset of Spanish households: insights into consumption patterns

An electricity smart meter dataset of Spanish households: insights into consumption patterns

Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Contact Info

Product

Resources

About