Abstract
This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.

Abstract
Electrical distribution utilities have been dealing with the problem of estimation of distribution network load diagrams, either for operation studies or in forecasting models for planning purposes. Load curve assessment is essential for an efficient management of electric distribution systems. However, the only information available for most of the loads (namely LV loads) is related to monthly energy consumption. The general procedure uses measurements in consumers to construct inference engines that predict load curves using commercial information. This paper presents a new approach for this problem, based on Kohonen maps and Artificial Neural Networks (ANN) to estimate load diagrams for the Portuguese distribution utilities. A method for estimating error bars is also proposed in order to provide a high order information about the performance of load curve estimation process. Performance attained is discussed as well as the method to achieve confidence intervals of the main predicted diagrams. © Springer-Verlag Berlin Heidelberg 1997.

Abstract
In a competitive energy market environment, the procedure for fair loss allocation constitutes a matter of considerable importance. This task is often based on rough principles, given the difficulties on the practical implementation of a fairest process. This paper proposes a methodology based on neural networks for the distribution of power distribution losses among the loads. The process is based on the knowledge of load profiles and on the usual consumption measures. Simulations ere carried out for a typical MV network, with an extensive variety of load scenarios. For each scenario, losses were calculated and distributed by the consumers. The allocation criterion is established assuming a distribution proportional to the squared power. Finally, a neural network is trained in order to obtain a fast and accurate losses allocation. Illustrative results support the feasibility of the proposed methodology.

Abstract
Classifying consumers, namely LV consumers, in order to assign them typical load diagrams, was always a concern of the electric utilities, which used this kind of information to better manage their distribution networks. Now, with the transition to a completely open market, the need for settlement between distribution operators and traders requires hourly consumption records that are not generally available, so deriving load diagrams for LV consumers is a mandatory task. This paper presents a new methodology for this purpose that uses typical diagrams obtained in measurement campaigns to create classes defined in the commercial information space that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP (the Portuguese distribution operator) and the result will probably be adopted by the regulatory authority. © 2005 ISAP.

Abstract
This paper describes the methodology and results obtained in the studies developed for deriving loss profiles for the Portuguese electricity market. For each voltage level (LV, MV, HV and VHV) the losses were distributed by the corresponding global load diagram, proportionally to the square of the hourly consumption. Transformer losses are assigned to the consumers of voltage levels equal or smaller to the secondary voltage. Loss profiles (like load profiles) were developed for each specific year, with its calendar particularities, and the global energy balance expected for that year. A subsequent product of the adopted methodology is the set of loss factors, which are directly driven from these profiles. The methodology was developed in a project with EDP (the Portuguese distribution system operator) and the result was approved by the regulatory authority that adopted the proposed loss profiles for market use.

Abstract
In open energy markets, the settlement process between distribution operators and traders is made on an hourly (or 15 min) basis, while LV consumers' billing data continues to result from monthly energy bills. In order to reconcile these two different realities, load profiling is used as a means to redistribute the consumed energy of each trader's portfolio by hourly intervals, according to recorded consumption patterns. This paper presents a new clustering approach to derive typical load diagrams that can be used in the process. The algorithm uses real load diagrams obtained in measurement campaigns to define classes (in the billing information space) that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP Distribution (the Portuguese distribution system operator) and the result was approved by the Regulatory Authority that adopted the proposed profiles for market use.