Analysis of ensemble models in the medium term hydropower scheduling

The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits throughout the planning period, while respecting plant operational constraints. Besides, it is important to emphasize that this decision-making has been done based mainly on inflow earliness knowledge. To perform the forecast of a determinate basin, it is possible to use some intelligent computational approaches. In this paper one considers the Dynamic Programming (DP) with the inflows given by their average values, thus turning the problem into a deterministic one which the solution can be obtained by deterministic DP (DDP). The performance of the DDP technique in the MTHS problem was assessed by simulation using the ensemble prediction models. Features and sensitivities of these models are discussed. © 2012 IEEE.

Methodology to forecast the harvest date of banana bunches

Introduction. This method is used to forecast the harvest date of banana bunches from as early as the plant shooting stage. It facilitates the harvest of bunches with the same physiological age. The principle, key advantages, time required and expected results are presented. Materials and methods. Details of the four steps of the method ( installation of the temperature sensor, tagging bunches at the flowering stage, temperature sum calculation and estimation of bunch harvest date) are described. Possible problems are discussed. Results. The application of the method allows drawing a curve of the temperature sum accumulated by the bunches which have to be harvested at exactly 900 degree-days physiological age.

Overflow characteristics of circular weirs: Effects of inflow conditions

The most common types of weirs are the broad-crested weir, the sharp-crested weir, the circular-crested weir, and nowadays, the ogee crest weir, Advantages of the cylindrical weir shape include the stable overflow pattern, the ease to pass floating debris, the simplicity of design compared to ogee crest design, and the associated lower costs. in this study, the writers describe new experiments of circular weir overflows, with eight cylinder sizes, for several weir heights and for five types of inflow conditions: partially developed inflow, fully developed inflow, upstream ramp, upstream undular hydraulic jump, and upstream (breaking) hydraulic jump. Within the range of the experiments, the cylinder size, the weir height DIR and the presence of an upstream ramp had no effect on the discharge coefficient, flow depth at crest, and energy dissipation. But the inflow conditions had substantial effects on the discharge characteristics and flow properties at the crest. Practically, the results indicate that discharge measurements with circular weirs are significantly affected by the upstream flow conditions.

Influence of Parasympathetic Modulation in Doppler Mitral Inflow Velocity in Individuals without Heart Disease

Background: The relation between left ventricular filing velocities determined by Doppler echocardiography and autonomic nervous system function assessed by heart rate variability (HRV) is unclear. The aim of this study was to evaluate the influence of the autonomic nervous system assessed by the time and frequency domain indices of HRV in the Doppler indices of left ventricular diastolic filling velocities in patients without heart disease. Methods: We studied 451 healthy individuals (255 female [56.4%]) with normal blood pressure, electrocardiogram, chest x-ray, and treadmill electrocardiographic exercise stress test results, with a mean age of 43 +/- 12 (range 15-82) years, who underwent transthoracic Doppler echocardiography and 24-hour electrocardiographic ambulatory monitoring. We studied indices of HRV on time (standard deviation [SD] of all normal sinus RR intervals during 24 hours, SD of averaged normal sinus RR intervals for all 5-minute segments, mean of the SD of all normal sinus RR intervals for all 5-minute segments, root-mean-square of the successive normal sinus RR interval difference, and percentage of successive normal sinus RR intervals > 50 ms) and frequency (low frequency, high frequency, very low frequency, low frequency/high frequency ratio) domains relative to peak flow velocity during rapid passive filling phase (E), atrial contraction (A), E/A ratio, E-wave deceleration time, and isovolumic relaxation time. Statistical analysis was performed with Pearson correlation and logistic regression. Results: Peak flow velocity during rapid passive filling phase (E) and atrial contraction (A), E/A ratio, and deceleration time of early mitral inflow did not demonstrate a significant correlation with indices of HRV in time and frequency domain. We found that the E/A ratio was < 1 in 45 individuals (10%). Individuals with an E/A ratio < 1 had lower indices of HRV in frequency domain (except low frequency/high frequency) and lower indices of the mean of the SD of all normal sinus RR intervals for all 5-minute segments, root-mean-square of the successive normal sinus RR interval difference, and percentage of successive normal sinus RR intervals > 50 ms in time domain. Logistic regression demonstrated that an E/A ratio < 1 was associated with lower HF. Conclusion: Individuals with no evidence of heart disease and an E/A ratio < 1 demonstrated a significant decrease in indexes of HRV associated with parasympathetic modulation. (J Am Soc Echocardiogr 2010;23: 762-5.)

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.