Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarterhourly values of the Universitat Politècnica de València, a commercial customer consuming 11,500 kW.

In rural areas or in isolated communities in developing countries it is increasingly common to install micro-renewable sources, such as photovoltaic (PV) systems, by residential consumers without access to the utility distribution network. The reliability of the supply provided by these stand-alone generators is a key issue when designing the PV system. The proper system sizing for a minimum level of reliability avoids unacceptable continuity of supply (undersized system) and unnecessary costs (oversized system). This paper presents a method for the accurate sizing of stand-alone photovoltaic (SAPV) residential generation systems for a pre-established reliability level. The proposed method is based on the application of a sequential random Monte Carlo simulation to the system model. Uncertainties of solar radiation, energy demand, and component failures are simultaneously considered. The results of the case study facilitate the sizing of the main energy elements (solar panels and battery) depending on the required level of reliability, taking into account the uncertainties that affect this type of facility. The analysis carried out demonstrates that deterministic designs of SAPV systems based on average demand and radiation values or the average number of consecutive cloudy days can lead to inadequate levels of continuity of supply.

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Many efforts have been made to define patterns, predict, and forecast energy use. However, changes in energy consumption may be studied in detail using various methodologies. This work presents a statistical methodology to assess changes in a facility consumption profile. Consumption patterns are obtained from a historical database of a predefined time interval, according to the type of day (day of the week, working or non-working), and an index that assesses change in the electrical consumption profile is proposed. Assessing these changes enables associating these values with possible events in a facility, which can serve to generate alarms in an energy management system, and reduce costs and maintenance periods. Additionally, a multi-criteria interpretation of the applied test table is presented that offers explanations and identifies possible causes of anomalous consumption.

The calculation of electricity consumption forecast a few days ahead is a complicated issue and studies about this matter are continually being performed. Advances in this field allow obtaining consumption forecasts increasingly accurate. These consumption forecasts aim to improve the knowledge of the facilities, the planning and control of consumption and the measurement and verification of energy saving measures, among others. In this study the authors present several advances related to consumption forecast using end-uses (EUs). The methodology described enables an easy disaggregation of each EU in a facility and it also enables calculating a good forecast for each of them. For the disaggregation process, the correlation between energy and external variables, such as mean temperature, degree days or daylight, is studied. Additionally, an extrapolation method to obtain a total consumption forecast from forecasted EUs that cover approximately 60% of total consumption is developed. With this procedure, total consumption forecasts with high accuracy can be obtained without the need of classifying more than the 60% of the consumption of a facility. The higher accuracy in each end-use, the better results are obtained in the total consumption forecast. For this reason, the study is focused in the end-uses disaggregation and its forecast calculation. The entire methodology is illustrated and contrasted using the consumption of the Universitat Politècnica de València.