Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks.

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.

Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System.

This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%.

Boundedly rational expected utility theory.

We build a satisficing model of choice under risk which embeds Expected Utility Theory (EUT) into a boundedly rational deliberation process. The decision maker accumulates evidence for and against alternative options by repeatedly sampling from her underlying set of EU preferences until the evidence favouring one option satisfies her desired level of confidence. Despite its EUT core, the model produces patterns of behaviour that violate standard EUT axioms, while at the same time capturing systematic relationships between choice probabilities, response times and confidence judgments, which are beyond the scope of theories that do not take deliberation into account.

[Prevalence of anemia among schoolchildren in the province of Kenitra in Morocco]. / Prévalence de l'anémie chez les préadolescents scolaires dans la province de Kénitra au Maroc.

UNLABELLED: Anemia is a widespread public health problem in developed and developing nations, and its prevalence is highest among pregnant women. In developing countries, schoolchildren constitute the population with the next-highest prevalence. Because there are few studies of anemia in schoolchildren in Morocco, this study aimed to determine its prevalence and its risk factors among preadolescents. We recruited 306 pupils from seven primary schools; blood samples were taken with their parents' or guardians' consent. We also collected anthropometric data, information about social and demographic characteristics (parent questionnaire) and school attendance and performance. RESULTS: More than 30% of these children had anemia: prevalence did not differ by sex, but was higher among those living in urban environments. Factors related to food behavior, especially diet diversity, appeared to be important. Our results found no relation between anemia and school performance. In the future more detailed cognitive tests should be used for this type of study. CONCLUSION: The prevalence of anemia among schoolchildren is high in the province of Kénitra, and the school health system is weak. Decision-makers have recently become aware of the need for an integrated approach to this age group: schools offer an opportunity for prevention and cure.