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The study was conducted in a one-stage rotating electrobiological disk contactor (REBDC) and a rotating electrochemical disk contactor (RECDC). Synthetic wastewater with characteristics similar to the wastewater from soil-less cultivation of tomatoes was used in the experiment. Current efficiency (CE) values, that express the denitrification performance of bio- and electrochemical reactors, were higher in the electrobiological contactor than in the electrochemical one. Combining biological processes with electrochemical processes in the electrobiological contactor resulted in almost 20% higher current efficiency in the contactor operated at a density of 0.63 A/m2 and hydraulic retention time (HRT) = 4 h. The study showed that, in both the electrochemical and the electrobiological contactor, current density increase and hydraulic retention time extension increased electric power consumption (E) during phosphorus compounds removal and simultaneously lowered current efficiency.

As the actual grid must be improved and integrate variable renewable energy (VRE) into it, different methods and strategies have been developed. These techniques permit higher flexibility, better stability and, therefore, a larger penetration of renewable energy in the grid. An important technique to make this happen is demand forecasting. This technique is very common and there are many projects related to it, integrated with demand side management (DSM) or used to set energy prices. All these models are usually used for short periods of time, however, long-term forecasting models are not as common. The main problem is that the longer the forecast period, the greater the uncertainty and the accuracy of the models start being unacceptable. This article tries to find the most accurate and fastest models for long-term demand forecasting and compare the different methods. One of the main tools used in the energy forecast methods is related to artificial intelligence, because since its conceptualization in 1940 [2], has been an important computational process that helps different sectors. Until this time, different companies, universities and organisations have developed different models in order to predict future energy values. The first question that arrives is: what is artificial intelligence and how to apply it in the energy sector? Artificial intelligence is a system that can re-organise or improve its decisions over time depending on the current circumstances. To do it, it is needed to collect and analyse large datasets (Big Data). The final aim of this system is to be able to find a solution without any human intervention. The year 1956 is considered the birth of AI, where the event Dartmouth Summer Research project was born[3]. Since this year different models were introduced and scientists realised that many important applications of AI needed a large amount of data that was tough to manage. Due to this fact, AI and Big Data are usually working together. The problem with the first models is that the results were not as good as needed because of the nature of the models used, but a new concept known as backpropagation captures again the attention of researcher groups to use again AI in the 1970s. This concept enhances the way that neural networks improve their accuracy over time, reducing their error. Moreover, the improvement of technology allows the use of heavier models and to use more data due to the simple access and the growth of Big Data and Smart meters. Even, being AI a good tool for demand forecasting, it is not only related to this. As AI can enable fast and intelligent decision-making, it can be added to different energy problems to solve them, increasing the grid flexibility. If the grid is separated into the generation, transportation and distribution parts, AI can be integrated into all of them doing different tasks. In the generation part, can be useful, for instance, for wind and solar forecasting. The management of Big Data can optimise and improve the accuracy of the forecast. The increase of AI in the generation part is pushed by two main innovation trends: decentralization (increase in the deployment of small power generators) and electrification (in transport and buildings mainly). In the transportation part, AI can help to improve grid stability and reliability. On the other side, in the distribution part, it can optimise the demand-side management. However, some key points must be solved before its integration such as the availability and quality of the data and the cybersecurity to avoid attacks. There are some examples of AI integrated into the grid such as EUPHEMIA, which is a coupling algorithm that integrates 25 European day-ahead energy markets. This algorithm tries to join the European electricity forecast markets but also HVAC load management with AI has been used in order to reduce the consumption in buildings[4]. After knowing some possibilities where AI can be integrated, it is possible to classify them, in six main categories[5]: 1. Improved renewable energy generation forecast 2. Maintain grid stability and reliability 3. Improved demand forecast 4. Efficient demand-side management 5. Optimised energy storage operation 6. Optimised market design and operation The one studied in this report is the improvement of demand forecast, which can enhance the demand-side management in the short-term but also helps political decisions in order to organise the elements needed in the grid. However, papers done in this research field are not as standardised and are not as easy as other papers to obtain conclusions from it or to compare with different scenarios. Commonly, authors considered neural networks as a black box where their relative features are not really taken into account but it is its knowledge what make the model work properly. Moreover, it is really common to find many papers that use hybrid algorithms in order to obtain better results because the accuracy of the combination is higher than its counterparts. On the other side, a common mistake is to overfit the models making some noise that can be avoided in order to improve the accuracy but this is difficult to evaluate if it is happening if the raw data is not given. Therefore, it is difficult to trust other papers or to obtain conclusions from them because the fact of extrapolating their results to other cases can be misleading. Different factors that can affect the results as the quality or size of the data, making the results of the paper very specific for this scenario and for this time. Furthermore, the quality of the papers is not the same even using same tools and, also, some authors exaggerate results or manipulate data in order to obtain better results[2]. One important aspect of this article is that different methods are compared for the same scenarios and with the same raw data, where the parameters used are just adapted for each model, making it more reliable and fair to compare between them. All the same, neural networks are not the only known tool to predict future values but also statistical methods are very well known and are integrated into different sectors for forecast purposes. The difference between them, among others, is that Artificial Neural Networks (hereafter referred to as ANN) parameters are found by the concept of backpropagation previously explained while the coefficients of statistical methods are optimised trying to obtain the lowest root mean square error or other feature that reduce the error of the model. Formerly, these forecasting processes were carried on by power companies because of their knowledge in the field, however, new sectors are taking part in the energy forecast because of their familiarity with the forecast field. Most of the forecasting competitions developed from the 1990s to the 2010s were won by unrelated power companies[2]. Truly, does not exist the best forecast tool for a specific field, but it is how the models are applied and developed what matters. Any statistical tool or ANN used by a professional will have better results than any other model developed by someone without experience. In this article, the models were divided into two main categories: the statistical methods and the ANN. In the state of arts, the fundamentals of the models are addressed and in the methodology section, the tools and the results are depicted. In order to compare them properly, two scenarios were addressed having different shapes and data sizes. On the other side, the models were created following a one-step prediction where the model predicts one value, calculates the root mean square error (hereafter referred to as RMSE) and will be added to the historical. Most of the models were developed just taking into account energy in the historical, i.e. using univariate models, but another parameter has been addressed as an input variable in some models: temperature. Both have a nonlinear relationship, when the temperature decreases, the load also increases because of heating needs, but when the temperature increases, the load also increases because of cooling needs. That fact can create confusion by leading one to think that nonlinear models will suit better than others, but the reality is that linear models work as well as nonlinear with nonlinear problems[2]. In the end, the results and comparisons are shown.

The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727, (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2&middot, sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2&middot, sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.

Nighttime light data are often used to estimate some socioeconomic indicators, such as energy consumption, GDP, population, etc. However, whether there is a causal relationship between them needs further study. In this paper, we propose a causal-effect inference method to test whether nighttime light data are suitable for estimating socioeconomic indicators. Data on electric power consumption and nighttime light intensity in 77 countries were used for the empirical research. The main conclusions are as follows: First, nighttime light data are more appropriate for estimating electric power consumption in developing countries, such as China, India, and others. Second, more latent factors need to be added into the model when estimating the power consumption of developed countries using nighttime light data. Third, the light spillover effect is relatively strong, which is not suitable for estimating socioeconomic indicators in the contiguous regions between developed countries and developing countries, such as Spain, Turkey, and others. Finally, we suggest that more attention should be paid in the future to the intrinsic logical relationship between nighttime light data and socioeconomic indicators.

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost This research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the ‘Smart IT Engineering and Solutions’ accredited research group (Generalitat de Catalunya, 2014 SGR 1052)

This paper presents an experimental analysis of classical control strategies applied to a small size thermoelectric refrigerator. First, the current-voltage characteristic of the thermoelectric module, installed in a commercial refrigeration unit, is measured and analyzed. Then, different types of controllers are compared to improve the unit performance in case of energy supply provided by a stand-alone DC system. Especially in case of power supply from intermittent generation, the controller can help to exploit the thermal capacity of the refrigeration unit by slightly varying the internal temperature. Experimental tests validate the simulations.

This work thoroughly evaluates the electric power consumption of a full scale, 3 × 923 m3 complete stirred tank reactor (CSTR) research biogas plant with a production capacity of 186 kW of electric power. The plant was fed with a mixture of livestock manure and renewable energy crops and was operated under mesophilic conditions. This paper will provide an insight into precise electric energy consumption measurements of a full scale biogas plant over a period of two years. The results showed that a percentage of 8.5% (in 2010) and 8.7% (in 2011) of the produced electric energy was consumed by the combined heat and power unit (CHP), which was required to operate the biogas plant. The consumer unit agitators with 4.3% (in 2010) and 4.0% (in 2011) and CHP unit with 2.5% (in 2010 and 2011) accounted for the highest electrical power demand, in relation to the electric energy produced by the CHP unit. Calculations show that 51% (in 2010) and 46% (in 2011) of the total electric energy demand was due to the agitators. The results finally showed the need for permanent measurements to identify and quantify the electric energy saving potentials of full scale biogas plants.

The aim of this paper is to examine people’s attitudes towards electricity and electricity consumption within the Norwegian market context. The empirical material is based on 18 in-depth interviews and eight focus group discussions carried out in two Norwegian towns. We draw on theories which assume that different types of logics motivate and guide behaviour. The results show that people’s views on electricity consumption vary widely. About half of the sample thinks of electricity as a commodity, following the market logic embedded in the Norwegian electricity system. According to “consumerists”, as long as people pay for consumption, they are entitled to use as much electricity as they want. The other half of our sample, referred to as “citizens”, questions the purely market-based system. They raise the question of fairness and think there is a need for a norm for appropriate consumption. Both groups regard Norwegian hydropower as common property belonging to the Norwegian population, and both groups question the existing market-based system for exchange of power with other countries. We use the results to discuss some of the policy implications in terms of how energy savings could be achieved., Consilience, No 11 (2014): Issue Eleven: 2014

This paper describes a procedure for measuring the combined electrical power consumption of an AWAC and a GSM modem. The purpose is to calculate the number of batteries and power required to provide and autonomy of 45 days. Experimental results show consumption in the order of 1 W/hr, and from this value adequate batteries are selected.