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Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting
(2023-01-01)
Liu, Zongying
;
Tahir, Ghalib Ahmed
;
Masuyama, Naoki
;
Kakudi, Habeebah Adamu
;
Fu, Zhongyu
Electricity is one of the most consumed commodities in the modern world. Electricity load prediction models are used to plan distribution operations to balance the equilibrium of demand and supply. This necessity has increased the number of recent research works. They employed several learning algorithms, such as support vector regression, to predict demands. However, these algorithms have high computational cost and too many user-defined parameters that directly impact their performance. Recently, randomization-based learning algorithms have been widely tested because they performed well at a lower cost. However, still, there was a main drawback: uncertainty in approximation and learning. This work employed a kernel trick to solve the uncertainty problem. A kernel with reservoir-state layers was used to solve the problem. The kernel reservoir-state layers from the echo state network not only transformed features into high-dimensional space, but also enhanced the forecasting ability by learning temporal information. Additionally, the proposed model also had a multi-step prediction ability that used previous forecasting errors to update the output weights in the current step to prevent an accumulated error problem. We compared our proposed model with single-layer and multi-layer variants of Extreme Learning Machine, Echo State Network, and Random Vector Functional Link on ten electrical load data sets. The proposed model showed the best performance on 9/10 data sets in terms of Mean Square Error or Symmetric Mean Absolute Percentage Error. These findings implied that the proposed algorithm was superior in forecasting long-term electricity load.
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A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction
(2021-03-01)
Liu, Zongying
;
Loo, Chu Kiong
;
Pasupa, Kitsuchart
With the development of industry and technology, the development of the environment and cities has drawn lots of attention. Time series prediction plays a vital role in protecting the environment and improving the level of intelligence and technology in cities, for example prediction of air pollution, water levels, palm oil prices, financial data and grid security. We describe a new algorithm, “Error-output Recurrent Two-layer Extreme Learning Machine” or ERT-ELM: it applied a new recurrent technique, that not only removed the restriction of the prediction horizon problem, but it also used a mean squared error of the current step to update the output weights for the next step. This technique avoided error accumulation in the original recurrent algorithm for multi-step time series prediction. Moreover, the new two-layer structure network improved forecasting compared to conventional single-layer or two-layer ELM models. Quantum behaved Particle Swarm Optimization was used to find suitable ERT-ELM parameters. The ability of our model was assessed on ten data sets—two artificial and eight real-world data sets and performed significantly better than the baselines. Especially for the synthetic data sets, in 1–18 prediction periods, our model achieved mean square errors of 2.64 ×10<sup>−3</sup> on the Mackey-Glass data set and 1.49 × 10<sup>−4</sup> on the Lorenz data sets.

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