Medium-term load forecasting is a useful tool for the maintenance planning of grids and as a market research of electric energy. In this work medium-term load forecasting methods are developed, the most forgotten time scaling process in the load forecasting bibliography. These methods will be applied to the peninsular Spanish monthly energy consumption. Methods traditionally employed with this objective are based on regression, statistical techniques (mainly Box-Jenkins ARIMA), and also with neural networks, fuzzy logic or expert systems. Most of them need the use of nonelectric variables, mainly climatic or economic ones, which strongly influence electric energy demand. These variables, of cyclic nature, provide a periodic behaviour to the energy consumption time series. This work presents a study of this periodic behaviour by means of spectral analysis, with the identification and interpretation of the dominant frequencies. A forecasting method for future values of electric energy demand will be then presented, which is based on a simple regression technique combined with neural networks. It does not take into account any climatic or economic variables, because only periodic behaviour of the time series is considered. Acceptable results are reached, with percentage errors lower than 5 % in most cases.

This paper describes a prototype of artificial olfactory system or electronic nose (NEONOSE) containing commercially available MOX and NDIR gas sensors, forming an array of a total of 13 sensors (six MOX and seven NDIR sensors). The electronic circuits have been designed for this application. The prototype consists of two boards (containing different type of sensors) with two microcontrollers that communicates each other by using a digital interface. The processed data is sent via Bluetooth to an Android smartphone, where it is collected and stored using an own developed application developed. Some measurements with different industrial gases have been done for testing the device.

In this work, the biomass productivity for biorefinery products and growth curves of three autochthonous microalgae collected in different reservoirs (“Scenedesmus sp.” (SSP), mixture of Scenedesmus sp., Chlorella minutissima, Chlorellas sp. and Nannochloropsis sp. named “La Orden” (LO) consortium and Chlorella minutissima named “Charca Brovales” (CB) consortium) were studied in a 5.5 L column laboratory photobioreactor. Two different culture media, Arnon culture (AM) and an agriculture fertilizer-based liquid medium (FM), have been used to evaluate the growth effect of the microalgae; it was found that the medium has a clear effect on the biomass productivity and growth rate, which ranged between 0.26–0.498 g L−1 d−1 and 0.288–0.864 d−1, respectively. In general, the elemental analysis and higher heating value of microalgae biomass for the three species were independent of the culture medium used for its growth, while their lipids and sugars content depended upon the species type and culture medium used in the cultivation. “La Orden” microalga was selected (given its best adaption to the climatic conditions) to study the biomass productivity and growth rate in two exterior photobioreactors (100 L column and 400 L flat panel), using FM as a medium, obtaining values of 0.116–0.266 g L−1 d−1 and 0.360–0.312 d−1, respectively. An automation and control system was designed to operate the exterior photobioreactors pilot plant. The lipid content of this microalga in these photobioreactors was lower than in the laboratory one, with a fatty acids profile with predominantly palmitic, oleic, linoleic and linolenic acids. Also, the fresh biomass collected from these photobioreactors was studied in a batch type digestion process for biogas production, obtaining a CH4 yield of 296 ± 23 L CH4 kgVSS−1 added with a reduction in percentage of COD and vs. of 50 ± 1% and 50 ± 1.7%, respectively.

Achieving Nearly Zero-Energy Buildings (NZEBs) is a main goal for the European Union, in order to reduce energy consumption in the building sector.NZEB means a building that has a very high energy performance.Its energy requirements should be covered by renewable sources, produced on-site or nearby [1].It could be possible if building were turned into a "small power generating station", or reducing consumption with passive building proposals.However, we think that it is worth looking for a balance between energy consumption and generation for every building, following this simple equation: Consumption = demandgenerationThe European regulations have already begun to indicate deadlines to implement NZEB requirements in buildings.Therefore, Spanish legislation related to energy efficiency and renewable energy generation in buildings has been recently updated, CTE HE [2].This paper provides a comparative analysis for the new requirements (2013 CTE DB HE compared with previous 2006 regulation, revised in 2009).This study was performed by using a computer building model, including its geometry, building materials, usage profiles and installations.Thus, we could compare the characteristics of the different regulations, and we could evaluate the progress toward the NZEB concept.

Electric energy demand forecasting represents a fundamental information to plan the activities of the companies that generate and distribute it. So a good prediction of its demand will provide an invaluable tool to plan their production and growth policies. This demand may be seen as a temporal series when its data are conveniently arranged. In this way the prediction of a future value may be performed studying the past ones. Neural networks have proved to be a very powerful tool to do this. They are mathematical structures that mimic that of the nervous system of living beings and are used extensively for system identification and prediction of their future evolution. In this work a neural network is presented to forecast the evolution of the monthly demand of electric consumption. Two strategies are proposed: the first uses a network that is trained once an then used to predict future values of the time series, while in the second the network is trained with all the past data every time a prediction is to be performed. The Spanish monthly consumption from 1975 to 2002 has been used to validate the models proposed. Errors smaller than 5% have been obtained in most of the predictions.

Electric energy demand forecasting represents a fundamental information to plan the activities of the companies that generate and distribute it. So a good prediction of its demand will provide an invaluable tool to plan the production and purchase policies of both generation and distribution or reseller companies. This demand may be seen as a temporal series when its data are conveniently arranged. In this way the prediction of a future value may be performed studying the past ones. Neural networks have proved to be a very powerful tool to do this. They are mathematical structures that mimic that of the nervous system of living beings and are used extensively for system identification and prediction of their future evolution. In this work a neural network is presented to predict the evolution of the monthly demand of electric consumption. A feedforward multilayer perceptron (MLP) has been used as neural model with backpropagation as learning strategy. The network has three hidden layers with a 8-4-8 distribution. It takes twelve past values to predict the following one. Errors smaller than 5% have been obtained in most of the predictions.