A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

黑龙江省生态足迹分析及ARIMA模型预测

为了解黑龙江省生态可持续发展问题,对黑龙江省生态足迹进行分析,并寻求动态预测结果。运用生态足迹模型对黑龙江省2000~2015年的人均生态足迹和生态承载力进行测算,在此基础上选用ARIMA模型,并结合使用Eviews软件对未来10年人均生态足迹和生态承载力进行预测。研究结果表明2000~2015年间黑龙江省人均生态足迹一直不断增加,人均生态承载力呈现波动缓慢上升的趋势,生态系统处于不安全状态;2016~2025年黑龙江省人均生态足迹仍然持续增大,虽然人均生态承载力也缓慢上升,但人均生态赤字仍然越来越大,黑龙江省生态安全面临巨大挑战。     

基于ARIMA模型的中国外贸进出口预测:2006-2010

ARIMA(Auto-regressive Moving Average)模型是一种常用的随机时序模型,主要用于预测,短期预测精度较高。本文利用ARIMA模型预测了2006-2010年中国外贸进出口总额、出口总额和进口总额。     

阶梯电价政策的居民节能意愿与节能效果评估--基于广东案例的研究

以广东省为例,基于粤东、粤西、粤北及珠三角典型地市的社会调查,统计分析阶梯电价政策对居民节能意愿及家庭用电的影响;同时运用广东省月度电力数据,构建ARIMA模型,定量研究政策实施的节能效果。研究表明,阶梯电价政策的实施对改善居民节能意愿有积极影响,并在短期内有明显的节能效果,但随着时间推移,节能效果有所减弱。为设计与完善相关政策,未来需要从节能意愿、经济激励等角度切入,提升政策的针对性、有效性。     

福建省GDP时间序列模型建立及实证分析

根据福建省1981—2011年GDP数据,利用Eviews6.0计量经济学软件和时间序列理论,建立了求和回归移动平均模型ARIMA(4,1,1),并根据模型进行实证分析,结果表明GDP预测效果较好,平均误差9.91%,并预测2014年GDP产值为22 569.578亿元。     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

基于组合模型的江苏省居民用电量预测分析

研究利用时间序列基本分析方法ARIMA模型分析法、指数平滑ETS模型和神经网络自回归模型对江苏省居民每月用电量进行数据分析、处理、拟合、检验及预测,以2004年1月至2017年12月用电计量数据作为分析样本,使用R软件对该时间序列进行建模。对给出的数据建立ARIMA模型、ETS模型和NNAR神经网络自回归模型,接着利用MAE、RMSE、MAPE三个评价指标来衡量模型的优良度。尝试通过组合模型对2018年江苏省居民12个月的用电量进行预测,与实际值进行对比验证,发现权重模型的误差最小,选择作为最终预测模型。最后得出结论,组合模型的预测效果要优于非组合模型。     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

Application of ANFIS to exchange rate forecasting

The need of exchange rate forecasting in order to preventing its disruptive movements has engrossed many policy-makers and economists for many years. The determinants of exchange rate have grown manifold making its behavior complex, nonlinear and volatile so that nonlinear models have better performance for its forecasting. In this study the accuracy of ANFIS as the nonlinear model and ARIMA as the linear models for forecasting 2, 4 and 8 days ahead of daily Iran Rial/∈ and Rial/US$ was compared. Using forecast evaluation criteria we found that nonlinear model outperforms linear model in all three horizons.     

ARIMA模型在杭州市全社会固定资产投资预测中的应用

本文介绍求和自回归移动平均模型ARIMA(p,d,q)的建模方法及Eviews实现。将ARIMA模型应用于杭州市全社会固定资产投资数据的分析与预测,得到较为满意的结果。