A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan

This paper proposes a hybrid methodology that exploits the unique strength of the seasonal autoregressive integrated moving average (SARIMA) model and the support vector machines (SVM) model in forecasting seasonal time series. The seasonal time series data of Taiwan’s machinery industry production values were used to examine the forecasting accuracy of the proposed hybrid model. The forecasting performance was compared among three models, i.e., the hybrid model, SARIMA models and the SVM models, respectively. Among these methods, the normalized mean square error (NMSE) and the mean absolute percentage error (MAPE) of the hybrid model were the lowest. The hybrid model was also able to forecast certain significant turning points of the test time series.     

A new adaptive fuzzy inference system for electricity consumption forecasting with hike in prices

Large increase or hike in energy prices has proven to impact electricity consumption in a way which cannot be drawn from historical data, especially when price elasticity of demand is not significant. This paper proposes an integrated adaptive fuzzy inference system (FIS) to estimate and forecast long-term electricity consumption when prices experience large increase. To this end, first a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Logarithmic linear regressions are estimated with historical data and used to construct an initial first-order TS-FIS. Then, in the adaptation phase, expert knowledge is used to define new fuzzy rules which form a new secondary FIS for electricity forecasting. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual electricity consumption in Iran where removing energy subsidies has resulted in a hike in electricity prices. Gross domestic product (GDP), population and electricity price are three inputs for the initial TS-FIS. A questionnaire survey was conducted to collect the expert estimation on possible change in electricity per capita, change in electricity intensity and the ratio of GDP elasticity to population elasticity when price hikes. Based on the information collected, a fuzzy rule base is formed and used to construct the secondary FIS which is used for electricity forecasting until 2016. Furthermore, the performance of the proposed model of this paper is compared with three other models namely ANFIS, ANN and one-stage regression in terms of their mean absolute percentage error. The comparison shows a superior performance for the proposed FIS model.     

Petroleum demand forecasting for Taiwan using modified fuzzy‐grey algorithms

In this paper, we adopt the exponentially weighted moving average (EWMA) method to develop the residual modification EWMA grey forecasting model REGM(1,1) and combines it with fuzzy theory to derive the fuzzy REGM or the FREGM(1,1) model. The proposed model is used to forecast annual petroleum demand in Taiwan. The experimental results show that the mean absolute percentage errors, median absolute percentage error, and symmetric mean absolute percentage error of FREGM(1,1) model are higher by 23.71, 12.26, and 23.06% respectively, compared with those obtained using the traditional GM(1,1) model.     

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO?+?MLP and GA-ACO?+?MLP hybrid models is compared with principal component analysis (PCA)?+?MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO?+?MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).     

基于混合贝叶斯SVM的电价分类与预测

电价的分类与预测是电力市场电价理论研究中的重要内容。该文提出了混合贝叶斯支持向量机方法(BE-SVM)，通过贝叶斯统计方法对电价进行分类，挖掘有效的数据信息，并结合支持向量机(SVM)技术预测现货电价数据，贝叶斯前验分布和后验分布用来估计SVM中的参数。通过比较模型BE-SVM、SVM 和神经网络(ANN)的预测结果，表明该文提出的BE-SVM方法提高了电价的预测精度，是一种有效的方法。     

A soft computing system for day-ahead electricity price forecasting

Hourly energy prices in a competitive electricity market are volatile. Forecast of energy price is key information to help producers and purchasers involved in electricity market to prepare their corresponding bidding strategies so as to maximize their profits. It is difficult to forecast all the hourly prices with only one model for different behaviors of different hourly prices. Neither will it get excellent results with 24 different models to forecast the 24 hourly prices respectively, for there are always not sufficient data to train the models, especially the peak price in summer. This paper proposes a novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. SOM is used to cluster the data automatically according to their similarity to resolve the problem of insufficient training data. SVM models for regression are built on the categories clustered by SOM separately. Parameters of the SVM models are chosen by Particle Swarm Optimization (PSO) algorithm automatically to avoid the arbitrary parameters decision of the tester, improving the forecasting accuracy. The comparison suggests that SOM–SVM–PSO has considerable value in forecasting day-ahead price in Pennsylvania–New Jersey–Maryland (PJM) market, especially for summer peak prices.     

基于组合模型的电能表需求预测模型研究

目前,供电公司对电能表年度采购需求数量和季(月)度用表需求数量的预测,普遍存在准确度不高的现象,容易导致出现结构性缺表或过剩问题。针对上述情况,本文提出利用极限梯度提升、随机森林和长短期记忆网络等经典的预测模型对电能表月度需求数量分别进行建模,再运用线性回归方法和误差倒数法将三种模型进行组合的需求预测方式。该方法不但突出了模型各自的优势,还实现了模型间的互补。利用清洗修正后的历史用表数量数据,通过提取数据特征,对组合模型不断进行训练,进一步优化了组合模型的参数和组合方式,验证了提升模型预测准确度的可能性。最后,经过与历史电能表实际需求数量的比较,证明组合模型可以有效提升电能表需求数量的预测准确度。     

Gene expression programming as a basis for new generation of electricity demand prediction models

This study proposes a new gene expression programming (GEP) approach for the prediction of electricity demand. The annual population, gross domestic product, stock index, and total revenue from exporting industrial products were used to predict the electricity demand of the same year in Thailand. Several statistical criteria were used to verify the validity of the model. Further, the contributions of the influencing variables to the prediction of the electricity demand were analyzed. Correlation coefficient, root mean squared error and mean absolute percent error were used to evaluate the performance of the model. In addition to its high accuracy, the derived model outperforms regression and other soft computing-based models.     

基于支持向量机的电力市场价格预测中的核函数比较

电价预测是电力市场中的一个重要研究课题。支持向量机(SVM)已被广泛应用于这一领域。然而,电力市场电价的高波动性和随机性等特征给支持向量机核函数的选择带来了挑战。本文在选择不同核函数的基础上,分别建立两个电力价格预测模型,并用真实电力市场价格数据对两个模型进行验证。实验结果表明,与其他支持向量机预测研究相比,本文精心选...     

Forecasting wind speed using empirical mode decomposition and Elman neural network

Because of the chaotic nature and intrinsic complexity of wind speed, it is difficult to describe the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD–ENN approach, a hybrid of empirical mode decomposition (EMD) and Elman neural network (ENN), is proposed to forecast wind speed. First, the original wind speed datasets are decomposed into a collection of intrinsic mode functions (IMFs) and a residue by EMD, yielding relatively stationary sub-series that can be readily modeled by neural networks. Second, both IMF components and residue are applied to establish the corresponding ENN models. Then, each sub-series is predicted using the corresponding ENN. Finally, the prediction values of the original wind speed datasets are calculated by the sum of the forecasting values of every sub-series. Moreover, in the ENN modeling process, the neuron number of the input layer is determined by a partial autocorrelation function. Four prediction cases of wind speed are used to test the performance of the proposed hybrid approach. Compared with the persistent model, back-propagation neural network, and ENN, the simulation results show that the proposed EMD–ENN model consistently has the minimum statistical error of the mean absolute error, mean square error, and mean absolute percentage error. Thus, it is concluded that the proposed approach is suitable for wind speed prediction.     

A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system

Big data mining, analysis, and forecasting always play a vital role in modern economic and industrial fields. Thus, how to select an optimization model to improve the forecasting accuracy of electricity price is not only an extremely challenging problem but also a concerned problem for different participants in an electricity market due to our society becoming heavily reliant on electricity. Many researchers developed hybrid models through the use of optimization methods, classical statistical models, artificial intelligence approaches and de-noising methods. However, few researchers aim to select reasonable samples and determine appropriate features when forecasting electricity price. Based on the Index of Bad Samples Matrix (IBSM), a novel method to dynamically confirm bad training samples, and the Optimization Algorithm (OA), DCANN and Updated DCANN are proposed in this paper for forecasting the day-ahead electricity price. This model is a hybrid system of supervised and unsupervised learning and creatively applies the idea of deleting bad samples and searching quality inputs to develop and learn, which is unlike BPANN, RBFN, SVM and LSSVM. Numerical results show that the proposed model is not only able to approximate the actual electricity price (normal or high volatility) but also an effective tool for h-step-ahead forecasting (h is less than 10) compared to benchmarks.     

Demand forecasting of perishable farm products using support vector machine

This article presents a new algorithm for forecasting demand for perishable farm products, based on the support vector machine (SVM) method. Since SVMs have greater generalisation performance and guarantee global minima for given training data, it is believed that support vector regression will perform well for forecasting demand for perishable farm products. In order to improve forecasting precision (FP), this article quantifies the factors affecting the sales forecast of perishable farm products based on the fuzzy theory, which is suitable for real situations. Numerical experiments show that forecasting systems with SVMs and fuzzy theory outperform the radial basis function neural network, based on the criteria of day absolute error, relative mean error and FP. Since there is no structured way to choose the free parameters of SVMs, the variational range of free parameters and the effects of the parameters on prediction performance are discussed in this article. Analysis of experimental results proves that it is advantageous to apply SVMs forecasting system in perishable farm products demand forecasting.     

Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14–18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods.     

组合预测模型在电力系统负荷预测中的应用研究

本文基于灰色预测模型、滑动平均模型和指数平滑模型这三种单一预测模型,采用方差-协方差策略,建立组合预测模型。然后结合老挝电力系统的概况,对老挝的全国年用电量进行预测和分析。结果表明,组合预测模型的预测精度明显高于各单一预测模型,即组合预测模型的相对误差小于各单一预测模型的相对误差,说明组合预测模型具有相当的适用性和优越性。     

基于EMD-PSO-LSSVM的碳排分解集成预测方法

二氧化碳排放量的发展趋势作为能够反映各国减排措施的指标之一,近些年来受到广泛关注.为了缓解碳排放数据的非线性和波动性对预测精度造成的影响,提出一种高效的分解集成预测方法用于预测二氧化碳的年排放量.碳排的原始序列数据被经验模态分解(empirical mode decomposition, EMD)方法分解为不同频率的振动模块和残差项,粒子群优化算法(particle swarm optimization, PSO)优化后的最小二乘支持向量机(least squares support vector machine, LSSVM)用于预测每个分解模块.选取世界上12个国家的真实碳排数据进行实例验证,预测结果表明:EMD方法能够有效提高碳排预测的精准度;与其他预测模型相比,分解集成预测方法能够将平均绝对误差(mean absolute error, MAE)的均值最少提高46.46%,最多提高90.09%,将平均Pearson相关系数(Pearson correlation coefficient, PCC)值最少提高10.45%,最多提高45.10%.     

Implementing a diffusion model optimized by a hybrid evolutionary algorithm to forecast notebook shipments

Taiwan computer firms need to forecast trends in notebook shipments. The Bass diffusion model has been successfully applied to describe the empirical adoption curve for many new products and technological innovations. In order to improve the parameter estimates, a hybrid evolutionary algorithm, which couples genetic algorithms (GAs) with particle swarm optimization (PSO), is proposed. This hybrid approach can produce more accurate estimates of the parameters for the Bass diffusion model. In addition, the price index plays an important role in the notebook market. Thus, the modified diffusion model is proposed to investigate the forecasting performance for notebook shipments. The results illustrate that a hybrid approach outperforms other methods such as nonlinear algorithm, GA and PSO in terms of mean absolute percentage error.     

A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.

     

A meta-heuristic framework for forecasting household electricity consumption

It may be difficult to model household electricity consumption with conventional methods such as regression due to seasonal and monthly changes. This paper illustrates a flexible integrated meta-heuristic framework based on Artificial Neural Network (ANN) Multi Layer Perceptron (MLP), conventional regression and design of experiment (DOE) for forecasting household electricity consumption. Previous studies base their verification by the difference in error estimation, whereas this study uses various error estimation methods and design of experiment (DOE). Moreover, DOE is based on analysis of variance (ANOVA) and Duncan Multiple Range Test (DMRT). Furthermore, actual data is compared with ANN MLP and conventional regression model through ANOVA. If the null hypothesis is accepted, DMRT is used to select either ANN MLP or conventional regression. However, if the null hypothesis is accepted then the proposed framework selects either the MLP or regression model based on the average of Minimum Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Mean Absolute Error (MAE). The significance of this study is the integration of ANN MLP, conventional regression and DOE for flexible modeling and improved processing, development and testing of household electricity consumption. Some of the previous studies assume that ANN MLP provide better estimation and others estimate electricity consumptions based on the conventional regression approach. However, this study presents a flexible integrated framework to locate the best model based on the actual data. Moreover, it would provide more reliable and precise forecasting for policy makers. To show the applicability and superiority of the integrated approach, annual household electricity consumption in Iran from 1974 to 2003 was collected for processing, training and testing purpose.     

基于改进的灰色模型的旅游需求预测研究

针对海南省旅游需求预测问题,对传统的灰马尔科夫模型进行改进,提出了一种动态优化子集模糊灰马尔科夫预测模型。该模型首先根据GM(1,1)模型预测结果的平均绝对误差百分比,通过输入子集法来确定最优输入子集个数;然后利用模糊集理论,将计算出的隶属度向量作为马尔科夫转移矩阵向量的权重,以修正预测值。为了能够根据时间推移进行预测,建立了等维递补的动态预测模型。实验以海南省各市县旅游饭店接待情况为例,验证了该模型可以有效地提高预测数据的准确性。     

A modified Lotka–Volterra model for competition forecasting in Taiwan’s retail industry

The retail industry is an important component of the supply chain of the goods and services that are consumed daily and competition has been increasing among retailers worldwide. Thus, forecasting the degree of retail competition has become an important issue. However, seasonal patterns and cycles in the level of retail activity dramatically reduce forecasting accuracy. This paper attempts to develop an improved forecasting methodology for retail industry competition subject to seasonal patterns and cycles. Using market share data and the moving average method, a modified Lotka–Volterra model with an additional constraint on the summation of market share is proposed. Furthermore, the mean absolute error is used to measure the forecasting accuracy of the market share. Real Taiwanese retail data from 1999 is used to validate the forecasting accuracy of our modified Lotka–Volterra model. Our methodology successfully mitigates errors from seasonal patterns and cycles and outperforms other benchmark models. These benchmarks include the Bass and Lotka–Volterra models for revenue or market share data, with or without using the moving average method. Our methodology assists the retail industry in the development of management strategies and the determination of investment timing. We also demonstrate how the Lotka–Volterra model can be used to forecast the degree of industry competition.