According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

Statistics and Data Analysis in Geology

http://pierrepinson.com/31761/Literature/foley2012.pdf

Current methods and advances in forecasting of wind power generation

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Forecasting daily urban electric load profiles using artificial neural networks

Monitoring of wind farms’ power curves using machine learning techniques

Urban microclimatic variations, along with a rapid reduction of unit cost of air-conditioning (AC) equipments, can be addressed as some of the main causes of the raising residential energy demand in the more developed countries. This paper presents a forecasting model based on an Elman artificial neural network (ANN) for the short-time prediction of the household electricity consumption related to a suburban area. Due to the lack of information about the real penetration of electric appliances in the investigated area and their utilization profiles it was not possible to implement a statistical model to define the weather and climate sensitivities of appliance energy consumption. For this reason an ANN model was used to predict the household electric energy demand of the investigated area and to evaluate the influence of the AC equipments on the overall consumption. The data used to train the network were recorded in Palermo (Italy) and include electric current intensity and weather variables as temperature, relative humidity, global solar radiation, atmospheric pressure and wind speed values between June 1, 2002 and September 10, 2003. The work pointed out the importance of a thermal discomfort index, the Humidex index , for a simple but effective evaluation of the conditions affecting the occupant behaviour and thus influencing the household electricity consumption related to the use of heating, ventilation and air conditioning (HVAC) appliances. The prediction performances of the model are satisfying and bear out the ability of ANNs to manage incomplete and noisy data, solve nonlinear problems and learn complex underlying relationships between input and output patterns.

Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area

Buildings are responsible for a large share of the energy consumption and environmental impacts worldwide. An assessment of the energy demand and environmental performance of building stocks at large spatial scales is increasingly needed for decision support in sustainable planning and policy making. While current bottom-up building stock models mainly focus on the operational energy use, a new research trend has introduced Life Cycle Assessment (LCA) for a more holistic environmental performance assessment including several stages of the building life cycle. This paper reviews a set of selected bottom-up LCA studies evaluating the environmental impact of building stocks at several scales, from urban to transnational. The selected studies were analyzed according to three common elements: building stock aggregation model, energy analysis and LCA. Two main stock aggregation approaches, archetypes and building-by-building, are applied to model individual buildings and extrapolate results at the stock level. Bottom-up energy models, either statistical or engineering-based, provide energy profiles for the operational phase of buildings. LCA is subsequently performed to assess the building environmental performance and support policy decisions. Current limitations and opportunities for the improvement of the LCA of large building stocks are finally addressed and discussed to support future works. Building stock aggregation models may be enhanced by addressing model calibration, integration with Geographic Information Systems (GIS) and stock dynamics models. Uncertainty propagation, sensitivity analysis and model simplification are recommended for energy models and LCA to bridge data gaps and reduce computationally intensive models. Future research is encouraged on the inclusion of climate change, evolution of the energy supply and integrated modeling for the LCA of building stocks.

Antonino Marvuglia

Papers

Current methods and advances in forecasting of wind power generation

Forecasting daily urban electric load profiles using artificial neural networks

Monitoring of wind farms’ power curves using machine learning techniques

Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area

Life Cycle Assessment of building stocks from urban to transnational scales: A review