The economic benefit of short-term forecasting for wind energy in the UK electricity market

In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around £5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators.     

Estimating urban trail traffic: Methods for existing and proposed trails

The paper presents findings from the most comprehensive, continuous trail monitoring system in the United States, a network of 30 infrared monitors on five multiuse trails in Indianapolis, Indiana operating for periods of one to more than four years. We describe variation in traffic across different trails and segments of individual trails and present traffic ratios that describe variations in traffic by month, day of week, and time of day. We also present regression models for estimating traffic from temporal, weather, socio-demographic, and urban form variables. We propose and validate procedures for use of traffic ratios to extrapolate hourly counts to annual estimates and use of regression models to estimate traffic. Our procedures provide estimates within 20–30% of actual counts. Limitations of the models are noted. Potential applications range from forecasting traffic on proposed new trails to assessing the need for safety improvements such as stop lights at intersections.     

A decision support system methodology for forecasting of time series based on soft computing

Exponential procedures are widely used as forecasting techniques for inventory control and business planning. A number of modifications to the generalized exponential smoothing (Holt-Winters) approach to forecasting univariate time series is presented, which have been adapted into a tool for decision support systems. This methodology unifies the phases of estimation and model selection into just one optimization framework which permits the identification of robust solutions. This procedure may provide forecasts from different versions of exponential smoothing by fitting the updated formulas of Holt-Winters and selects the best method using a fuzzy multicriteria approach. The elements of the set of local minima of the non-linear programming problems allow us to build the membership functions of the conflicting objectives. It is compared to other forecasting methods on the 111 series from the M-competition.     

Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms

Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r(2)=0.74 for testing.     

A dynamic meta-learning rate-based model for gold market forecasting

In this paper, an improved EMD meta-learning rate-based model for gold price forecasting is proposed. First, we adopt the EMD method to divide the time series data into different subsets. Second, a back-propagation neural network model (BPNN) is used to function as the prediction model in our system. We update the online learning rate of BPNN instantly as well as the weight matrix. Finally, a rating method is used to identify the most suitable BPNN model for further prediction. The experiment results show that our system has a good forecasting performance.     

A collaborative and artificial intelligence approach for semiconductor cost forecasting

Forecasting the unit cost of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the unit cost. In order to effectively forecast the semiconductor unit cost, a collaborative and artificial intelligence approach is proposed in this study. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own fuzzy neural networks to forecast the semiconductor unit cost based on their viewpoints. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, a radial basis function (RBF) network is used. The effectiveness of the proposed methodology is shown with a case study.     

Forecasting Construction Manpower Demand by Gray Model

Construction is a labor-intensive industry that is heavily reliant on the availability of local manpower. The construction workload also fluctuates in either a cyclical or random manner. As a result, there is always either a shortage or surplus of manpower. Although a number of good forecasting models have been developed, they require input of sufficient and good-quality data to produce accurate results. The aim of this paper is to explore the use of the gray model in forecasting construction manpower based on a limited amount of data. A wide range of forecasting models in the literature is first reviewed. A single-variable first-order gray model is then proposed to forecast construction manpower one quarter ahead. The model is tested using manpower data based on the Quarterly Report on General Household Survey published by the Census and Statistics Department of the HKSAR Government. Data from 64 quarters, covering the first quarter of 1992 to the fourth quarter of 2007, are included. A computer program is formulated to manipulate all of the calculations involved. Based on the minimum mean absolute percentage error (MAPE) criterion, it is found that the optimal sample size is 5. Based on the input of data from five quarters, the MAPE of the overall forecast is only 3.21%, and the maximum absolute percentage error is 8.92%. It is thus concluded that the gray model produces very accurate results. The results of this study also suggest that this model is applicable to forecasts of other time series particularly when limited data are available.     

Time Series Models for Forecasting Construction Costs Using Time Series Indexes

Construction often involves considerable time gaps between cost estimation and on-site operations. In addition, many operations are performed over a considerable period of time. Accordingly, estimating construction costs must consider the trend of costs in the market, where construction costs normally change over time. Insight into the trend of construction costs in the market, therefore, is beneficial, even critical, to the effective cost management of construction projects. In an effort to support such insight development, two time series models were built by analyzing time series index data and comparing them with existing methods in the present study. The developed time series models accurately predict construction cost indexes. In particular, the models respond sensitively and swiftly to a quick, large change of costs, which allows for accurate forecasting over the short- and long-term periods. Overall, the models are effective for understanding the trend of construction costs.     

Grey prediction with rolling mechanism for electricity demand forecasting of Turkey

The need for energy supply, especially for electricity, has been increasing in the last two decades in Turkey. In addition, owing to the uncertain economic structure of the country, electricity consumption has a chaotic and nonlinear trend. Hence, electricity configuration planning and estimation has been the most critical issue of active concern for Turkey. The Turkish Ministry of Energy and Natural Resources (MENR) has officially carried out energy planning studies using the Model of Analysis of the Energy Demand (MAED). In this paper, Grey prediction with rolling mechanism (GPRM) approach is proposed to predict the Turkey's total and industrial electricity consumption. GPRM approach is used because of high prediction accuracy, applicability in the case of limited data situations and requirement of little computational effort. Results show that proposed approach estimates more accurate results than the results of MAED, and have explicit advantages over extant studies. Future projections have also been done for total and industrial sector, respectively.     

Forecasting user demand for wireless services: A socio-economic approach for Europe

This article reports the findings of a study for the European Commission to forecast demand for mobile communications services up to 2020. The study used a socio-economic approach including scenarios to explore the future and an original methodology for estimating traffic volumes under different socio-economic conditions. This article describes the objectives, outlines the methodology and summarises the results of the study. The study’s findings lay the foundation for a European consensus for calculating future spectrum requirements and are an important part of the process in the lead up to the World Radiocommunication Conference in 2007.