Data-based mechanistic modelling, forecasting, and control.

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecastin

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

The yield curve and the macro-economy across time and frequencies

This paper assesses the relation between the yield curve and the main macroeconomic variables in the U.S. between early 1960s and 2010 across time and frequencies, using wavelet analyses. The shape of the yield curve is modelled by latent factors corresponding to its level, slope and curvature, estimated by maximum likelihood with the Kalman filter. The macroeconomic variables measure economic activity, unemployment, inflation and the fed funds rate. The cross wavelet tools employed — coherency and phase difference —, the set of variables and the length of the sample, allow for a thorough appraisal of the timevariation and structural breaks in the direction, intensity, synchronization and periodicity of the relation between the yield curve and the macro-economy. Our evidence establishes a number of new stylized facts on the yield curve-macro relation; and sheds light on several results found in the literature, which could not have been achieved with analyses conducted strictly in the time-domain (as most of the literature) or purely in the frequency-domain.Macro-finance; Yield curve; Kalman filter; Continuous wavelet transform; Wavelet coherency; Phase-difference.

A wavelet approach to the dynamic relation between the Portuguese Yield Curve and Macroeconomic Growth

The goal of this work project is to discuss and analyze the relation between the components of the Portuguese yield curve and the economy’s level of activity for the period between 1996 and 2018. Based on traditional parametric methods, the macro-finance mode developed includes a dynamic latent factor model containing the conventional latent factors level, slope and curvature. The behavior of these variables is simultaneously analyzed in the time and frequency domains, using for that purpose wavelet transforms and wavelet tools. By applying the wavelet transformation to all time series data under analysis it possible to study the dynamic relationship among them in terms of direction, intensity, synchronization and periodicit

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

The yield curve and the macro-economy across time and frequencies

This paper assesses the relation between the yield curve and the main macroeconomic variables in the U.S. between early 1960s and 2009 across time and frequencies, using wavelet analyses. The shape of the yield curve is modelled by latent factors corresponding to its level, slope and curvature, estimated by maximum likelihood with the Kalman filter. The macroeconomic variables measure econmic activity, unemployment, inflation and the fed funds rate. The cross wavelet tools employed - coherency and phase difference - , the set of variables and the length of the sample, allow for a thorough appraisal of the time- variation and structural breaks in the direction,intensity,synchronization and periodicity of the relation between the yield curve and the macro-economy. Our evidence establishes a number of new stylized facts on the yield curve-macro relation; and sheds light on several results found in the literature, which could not have been achieved with analyses conducted strictly in the time-domain(as most of the literature)or purely in the frequency-domain.Macro-finance; Yield curve; Kalman filter; Continuous wavelet transform;Wavelet coherency;Phase-difference

Short-term forecasting of electricity consumption using Gaussian processes

Forecasting of electricity consumption is considered as one of the most signi cant aspect of e ective management of power systems. On a long term basis, it allows decision makers of a power supplying company to decide when to build new power plants, transmission and distri- bution networks. On a short term basis, it can be used to allocate resources in a power grid to supply the demand continuously. Forecasting is basically divided into three categories : short-term, medium-term, and long- term. Short-term refers to an hour to a week forecast, while medium-term refers to a week to a year, and predictions that run more than a year refers to long-term. In this thesis, we forecast electricity consumption on a short-term basis for a particular region in Norway using a relatively novel approach: Gaussian process. We design the best feature vector suitable for forecasting electricity consumption using various factors such as previous consumptions, temperature, days of the week and hour of the day. Moreover, feature space is scaled and reduced using reduction and normalization methods, and di erent target variables are analysed to obtain better accuracy. Furthermore, GP is compared with two traditional forecasting techniques : Multiple Back- Propagation Neural Networks (MBPNN), and Multiple Linear Regression (MLR). Finally we show that GP is as better as MBPNN and far better than MLR using empirical results

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

The habit-driven life: Accounting for inertia in departure time choices for commuting trips

This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals’ inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected