C. Senabre

Short-Term Predictability of Load Series: Characterization of Load Data Bases

This paper proposes the use of two indicators of the predictability of the load series along with an accuracy value such as mean average percentage error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these models provide a description of the inner design of the model, the results from applying this model to a specific data base and the conclusions drawn from this application. However, a single accuracy value may not be sufficient to describe the performance of the model when applied to other data bases. The aim of this paper is to provide researchers with a tool that is able to assess the predictability of a load series and, therefore, contextualize the forecasting accuracy reported. Nine different data bases from the U.S. have been used; all of them include hourly load and temperature data.

Development of a methodology for improving the effectiveness of customer response policies through electricity-price patterns

The main objective of electricity regulators when establishing electricity markets is to decrease the cost of electricity through competition. However, this scenario can not be achieved without a full participation of the electricity demand by reacting against electricity prices. The aim of this research is to develop tools for helping customers and aggregators to join price and demand response programs, while helping them to hedge against the risk of short-term price volatility. In this way, the capacity of and hybrid methodology (self-organizing maps and statistical wards linkage) to classify high electricity market prices is analyzed. Besides, with the help of non-parametric estimation, some price-patterns were found in the above mentioned clusters. The knowledge contained within these patterns supplies customer market-based information on which to base its energy use decisions. The interest for this participation of customers in markets is growing in developed countries to obtain a higher elasticity in demand. Results show the capability of this approach to improve data management and select coherent policies to accomplish cleared demand offers amongst different price scenarios in a more flexible way.

Using a Self Organizing Map Neural Network for Short-Term Load Forecasting, Analysis of Different Input Data Patterns

This research uses a Self-Organizing Map neural network model (SOM) as a short-term forecasting method. The objective is to obtain the demand curve of certain hours of the next day. In order to validate the model, an error index is assigned through the comparison of the results with the real known curves. This index is the Mean Absolute Percentage Error (MAPE), which measures the accuracy of fitted time series and forecasts. The pattern of input data and training parameters are being chosen in order to get the best results. The investigation is still in course and the authors are proving different patterns of input data to analyze the different results that they will be obtained with each one. Summing up, this research tries to establish a tool that helps the decision making process, forecasting the short-term global electric load demand curve.

“Self-organizing maps” for identification of tire model longitudinal braking parameters of a vehicle on a roller brake tester and on flat ground

This paper discusses how to identify tire model coefficients that are used to compare longitudinal forces when braking. Those in the automotive world have worked extremely hard to obtain these parameters and different methods have been used to match the values of these parameters. This paper proposes the use of self-organizing maps to tackle this problem whereby interactively searches are carried out to find the optimum tire model parameters. The objective of this research is to prove the capability of self-organizing maps (SOMs) to classify a vehicle’s braking formula on a roller brake tester from the MOT (Ministry of Transport) and on flat ground. The neural network produced a good brake-slip ratio when presented with data that are not used in network training. This means that the methodology is feasible. This tool easily obtains the brake-slip equation of each experiment and the braking on two different experimental tests will be compared.

Short-term load forecasting: Revising how good we actually are

This paper proposes the use of an indicator of the predictability of the load series along with an accuracy value such as Mean Average Percentage Error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these models provide a description of the inner design of the model, the results from applying this model to a specific data base and the conclusions drawn from this application. However, a single accuracy value may not be sufficient to describe the performance of the model when applied to other data bases. The aim of this paper is to provide researchers with a tool that is able to assess the predictability of a load series and, therefore, contextualize the forecasting accuracy reported. Thirteen different data bases were used to determine its validity.

SOM for Getting the Brake Formula of a Vehicle on a Brake Tester and on Flat Ground

The objective of the research is to prove the capability of Self-Organizing Map (SOM) to classify brake formula of a vehicle on a bank of roller tester from the MOT (Ministry of transport) and on flat ground. The neural network demonstrated good generation of the brake-slide relationship when presented with data not used in network training. This tool will easily find brake-slide equation of each experience and we will compare the brake on two different experimental tests. This article demonstrates that the MOT brake testing do not check the car brake in its usual way of driving. We will provide data and graphs to prove that tyre pressure is a determining factor when assessing the condition of brakes.