Add to ORKGEnergy applications are a fascinating source of prediction and other problems that exhibit nonlinearities, time delays, and nonstationary statistics. This makes them an ideal testbed for Extreme Learning Machines approaches. Some illustrative examples are reviewed, and some novel regulation approaches to condition data for ELM are also discussed
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networ...
This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM)...
This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the moder...
In this paper, Extreme Learning Machine (ELM) is demonstrated to be a powerful tool for electricity ...
We propose a multi-resolution selective ensemble extreme learning machine (MRSE-ELM) method for time...
As a novel and promising learning technology, extreme learning machine (ELM) is featured by its much...
This thesis introduces novel fast learning algorithms for neural networks namely extreme learning ma...
The techniques and theories of the Extreme Learning Machines (ELM) have been developing fast with th...
machine learning and artifi cial intelligence relies on the coexistence of three necessary condition...
Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural network...
Most countries in the world rely heavily on coal, oil and natural gas for its energy. But they are n...
This paper introduces intermittent learning - the goal of which is to enable energy harvested comput...
In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layer...
The machine learning techniques have been extensively studied in the past few decades. One of the mo...
Electricity price forecast is of great importance to electricity market participants. Given the soph...
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networ...
This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM)...
This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the moder...
In this paper, Extreme Learning Machine (ELM) is demonstrated to be a powerful tool for electricity ...
We propose a multi-resolution selective ensemble extreme learning machine (MRSE-ELM) method for time...
As a novel and promising learning technology, extreme learning machine (ELM) is featured by its much...
This thesis introduces novel fast learning algorithms for neural networks namely extreme learning ma...
The techniques and theories of the Extreme Learning Machines (ELM) have been developing fast with th...
machine learning and artifi cial intelligence relies on the coexistence of three necessary condition...
Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural network...
Most countries in the world rely heavily on coal, oil and natural gas for its energy. But they are n...
This paper introduces intermittent learning - the goal of which is to enable energy harvested comput...
In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layer...
The machine learning techniques have been extensively studied in the past few decades. One of the mo...
Electricity price forecast is of great importance to electricity market participants. Given the soph...
Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networ...
This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM)...
This paper presents a new approach to nontechnical loss (NTL) analysis for utilities using the moder...