A hole in the Vacuum Hose of a Vehicle Provides Lower Differences in Brake Measurements by the Ministry of Transport Brake Testers Rather Than the Characteristics of the Tester Used

In this research it is studied how vary brake measurement of a vehicle at Ministry of transport facilities when there is a hole in the vacuum hose of the brake booster. Data of brake with the brake boosting system in bad conditions is compared with results of brake of the same repaired vehicle tested on three different roller bed testers from the Maha brand. Finally, the Efficiency of passing the test with and without the hole in the vacuum hose is calculated to see differences

Feasibility of Recharging Electric Vehicles With Photovoltaic Solar Panels

AbstractThere are many reasons for the development and the use of renewable energy sources, such as the public awareness in the fight against climate change, energy independence with the security of supply, national competitiveness, technological development and job creation in a sector that has a great future. In this line, and within the proposed electric vehicle sustainability, it is an alternative to achieve a reduction of pollutant emissions and to increase the efficiency of road transport.The article presents a study of the use of electric vehicles from different points of view. It has been compared combustion vehicles with the electric counterparts in terms of power and features appreciated by the user in the automobile market.The purpose of the study was to analyze the feasibility to recharge different electric vehicles by solar photovoltaic modules, so that energy generation would not contribute to any CO2 emissions, when the system would be installed and ready to supply these vehicles. The study also shows a comparative analysis of the cost of purchasing electricity to the distributor compared with the using of a photovoltaic system designed to recharge the vehicle, even it has also been calculated the depreciation.Finally, it has been analyzed comparatively the type of the solar photovoltaic system considered more economically viable for recharging a pure electric vehicle (EV) therefore it has been compared projects on houses and on a parking to recharge several vehicles

Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study

Short-Term Load Forecasting is a very relevant aspect in managing, operating or participating an electric system. From system operators to energy producers and retailers knowing the electric demand in advance with high accuracy is a key feature for their business. The load series of a given system presents highly repetitive daily, weekly and yearly patterns. However, other factors like temperature or social events cause abnormalities in this otherwise periodic behavior. In order to develop an effective load forecasting system, it is necessary to understand and model these abnormalities because, in many cases, the higher forecasting error typical of these special days is linked to the larger part of the losses related to load forecasting. This paper focuses on the effect that several types of special days have on the load curve and how important it is to model these behaviors in detail. The paper analyzes the Spanish national system and it uses linear regression to model the effect that social events like holidays or festive periods have on the load curve. The results presented in this paper show that a large classification of events is needed in order to accurately model all the events that may occur in a 7-year period

Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy

This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%

Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns.This work was supported by European Union Sixth Frame work Program under Project EU-DEEP SES6-CT-2003-503516.Paper no.TPWRS-00633-200

Variability of data when testing the Handbrake Parking System vs the Electronic Park Brake with three Maha testers at Ministry of Transport facilities.

This research is focused on the test of the Electronic Parking Brake (EPB) and the hand brake at several Ministry of Transport (MOT) facilities to see if there are differences between data obtained. The parking brake force have been measured on three different brands of roller bed testers from MOT facilities such as: Maha, Ryme and Vteq. The rejection threshold is the same for all testers, 16% Efficency. Efficiency measured with all testers used were compared and also the MOT brake tester characteristics were compared to study correlations

A Physically-Based Model of Heat Pump Water Heaters for Demand Respose Policies: Evaluation and Testing

The development of Demand Response in residential segments is basic to develop a practical flexibility of demand, because these segments account for up to 40% of the overall demand. Energy Efficiency is another concern for these segments, but unfortunately present scenarios lack a practical coordination between Efficiency and Demand Response. This paper deals with an important problem in residential Demand Response: the determination of the flexibility and response on the demand-side, in this case through loads which can have a high potential for Demand Response and also a considerable interest for energy savings: Heat Pump Water Heaters. A residential load has been fully monitored (temperature, consumption, water flow) in the laboratory to obtain a Physically-Based Model which allows the evaluation of Demand Response options. Moreover, the model helps the aggregator obtain how the flexibility of demand (power, energy, energy payback or rebound effects) can be modified or limited, and how to deal with these characteristics and limitations to engage customers in Electricity Markets

Wheel Diameter and Width Influence in Variability of Brake Data Measurement at Ministry of Transport Facilities

The brake systems of vehicles are tested periodically by a “brake tester” at Ministry of Transport (MOT) stations. This tester measures the effectiveness of vehicle. This parameter is established by the International Committee of Vehicle Inspection (CITA). In this paper, we present an investigation of the influence of the tire size on the measurements of brake force on three MOT brake testers. We performed an analysis of the vehicle braking capacity test at MOT stations. The influence of varying wheel diameter and width on the measurement of braking at MOT stations has been analyzed. Thereby, the MOT brake tester as a verification system for a vehicle has been evaluated

Comparison of Short-Term Load Forecasting Performance by Neural Network and Autoregressive Based Models

In the past decade, many techniques ranging from statistical methods to complex artificial intelligence systems have been proposed by implementing their application to an electric system and highlighting its performance; usually providing a measure of accuracy like RMSE over a definite period. However, there is little research in which a fair comparison among methods is demonstrated, and it is difficult to determine which method would be better suited to a particular electric system or data set. This paper analysis one of the forecasting models running at the National Transport Operator of the Spanish system (REE), which is based on both autoregressive and neural network techniques. The results of this paper help to determine under which circumstances each of the models shows a better performance, which periods are more accurately forecasted by each model and provide valid criteria to choose one or the other depending on the characteristics of the application