Energy management system and supervision in a micro-grid using artificial neural network technique

Nowadays, the combination of conventional and renewable energy sources such as solar energy is one of the most widespread solutions to surmount the challenge of the climate and energy crisis. In the presence of random behavior of photovoltaic systems and variable power demand by consumers, energy management is a real challenge. In this paper, we propose a new energy management technique based on artificial neural networks in a smart grid. This will ensure the continuous supply of electricity to the consumer in the presence of random operation in energy consumption and generation. The global system is modeled and simulated under the MATLAB/Simulink tool.

Non-random behavior in sums of modular symbols

We give explicit expressions for the Fourier coefficients of Eisenstein series twisted by Dirichlet characters and modular symbols on [Formula: see text] in the case where [Formula: see text] is prime and equal to the conductor of the Dirichlet character. We obtain these expressions by computing the spectral decomposition of automorphic functions closely related to these Eisenstein series. As an application, we then evaluate certain sums of modular symbols in a way which parallels past work of Goldfeld, O’Sullivan, Petridis, and Risager. In one case we find less cancelation in this sum than would be predicted by the common phenomenon of “square root cancelation”, while in another case we find more cancelation.

Influence of the Iron as a Dopant on the Refractive Index of WO3

Results on studies of pure tungsten oxide WO3 and 2, 3 and 4% Fe-doped WO3 grown on the sapphire substrates by reactive pulsed laser deposition technique are reported. From X-ray diffraction it results that the crystalline structures changed with the substrate temperature and the peaks diffraction having a small shift by the amount of Fe content in WO3 lattice was noticed. Scanning electron microscopy presented a random behavior of WO3 nanocrystallites size with substrate temperatures. In the presence of 2% Fe-doped WO3, the nanocrystallites size varied gradually from 60 nm to 190 nm as substrate temperature increased. The transmission spectra of the pure and 2, 3 and 4% Fe-doped WO3 films were obtained within the 300–1200 nm spectral range. The refractive index of WO3 and Fe-doped WO3 layers were calculated by the Swanepoel method. The refractive index of pure WO3 shows a variation from 2.35–1.90 and for 2% Fe-doped WO3 from 2.30–2.00, as the substrate temperature increased. The contents of 3 and 4% Fe-doped WO3 presented nearly identical values of the refractive index with pure and 2% Fe-doped WO3, in error limits, at 600 °C. The optical band gap changes with substrate temperature from 3.2 eV to 2.9 eV for pure WO3 and has a small variation with the Fe.

Influence of memory processes on choice consistency

Choice-consistency is considered a hallmark of rational value-based choice. However, because the cognitive apparatus supporting decision-making is imperfect, real decision-makers often show some degree of choice inconsistency. Cognitive models are necessary to complement idealized choice axioms with attention, perception and memory processes. Specifically, compelling theoretical work suggests that the (imperfect) retention of choice-relevant memories might be important for choice-consistency, but this hypothesis has not been tested directly. We used a novel multi-attribute visual choice (MAVC) paradigm to experimentally test the influence of memory retrieval of exemplars on choice-consistency. Our manipulation check confirmed that our retention interval manipulation successfully reduced memory representation strength. Given this, we found strong evidence against our hypothesis that choice-consistency decreases with increasing retention time. However, quality controls indicated that the choice-consistency of our participants was non-discernable from random behavior. In addition, an exploratory analysis showed essentially no test-retest reliability of choice-consistency between two observations. Taken together, this suggests the presence of a floor effect in our data and, thus, low data quality for conclusively evaluating our hypotheses. Further exploration tentatively suggested a high difficulty of discriminating between the choice objects driving this floor effect.

Complexity and randomness in the Heisenberg groups (and beyond)

By studying the commuting graphs of conjugacy classes of the sequence of Heisenberg groups $H_{2n+1}(p)$ and their limit $H_\infty(p)$ we find pseudo-random behavior (and the random graph in the limiting case). This makes a nice case study for transfer of information between finite and infinite objects. Some of this behavior transfers to the problem of understanding what makes understanding the character theory of the uni-upper-triangular group (mod p) “wild.” Our investigations in this paper may be seen as a meditation on the question: is randomness simple or is it complicated?

Synthetic Series of Electricity Generation through a Photovoltaic System by using Different Panel Temperature Models

The electric energy produced by photovoltaic systems is a function of several variables that present random behavior, such as solar irradiance, ambient temperature, wind speed, and relative humidity. Several models describe the energy produced by these systems, which reflect the various factors that influence the generation and how significant these influences are. In this way, the present work aims to compare some panel temperature models and the electrical energy produced from a photovoltaic system located in the Midwest of Brazil. The applied methodology aims to evaluate the expected averages and standard deviations of the monthly synthetic series of generated energy to identify the main random variables that influence the electric generation. The results indicate the model that best describes the physical system concerning the randomness of the variables involved, the photovoltaic technology, the location, and the measurement period.

Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting

Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular location. Several models are used to forecast this type of time series with reasonable accuracy. However, due to the random behavior of these time series, achieving good forecasting performance represents a significant challenge. In this paper, we compare forecasting models to evaluate their accuracy in the short term using data on the EUR/USD exchange rate. For this purpose, we used three methods: Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) of the Elman type, and Long Short-Term Memory (LSTM). The analyzed period spanned from 2 January 1998, to 31 December 2019, and was divided into training and validation datasets. We performed forecasting calculations to predict windows with six different forecasting horizons. We found that the window of one month with 22 observations better matched the validation dataset in the short term compared to the other windows. Theil’s U coefficients calculated for this window were 0.04743, 0.002625, and 0.001808 for the ARIMA, Elman, and LSTM networks, respectively. LSTM provided the best forecast in the short term, while Elman provided the best forecast in the long term.

A pair correlation problem, and counting lattice points with the zeta function

The pair correlation is a localized statistic for sequences in the unit interval. Pseudo-random behavior with respect to this statistic is called Poissonian behavior. The metric theory of pair correlations of sequences of the form $$(a_n \alpha )_{n \ge 1}$$ ( a n α ) n ≥ 1 has been pioneered by Rudnick, Sarnak and Zaharescu. Here $$\alpha $$ α is a real parameter, and $$(a_n)_{n \ge 1}$$ ( a n ) n ≥ 1 is an integer sequence, often of arithmetic origin. Recently, a general framework was developed which gives criteria for Poissonian pair correlation of such sequences for almost every real number $$\alpha $$ α , in terms of the additive energy of the integer sequence $$(a_n)_{n \ge 1}$$ ( a n ) n ≥ 1 . In the present paper we develop a similar framework for the case when $$(a_n)_{n \ge 1}$$ ( a n ) n ≥ 1 is a sequence of reals rather than integers, thereby pursuing a line of research which was recently initiated by Rudnick and Technau. As an application of our method, we prove that for every real number $$\theta >1$$ θ > 1 , the sequence $$(n^\theta \alpha )_{n \ge 1}$$ ( n θ α ) n ≥ 1 has Poissonian pair correlation for almost all $$\alpha \in {\mathbb {R}}$$ α ∈ R .

A method of counteracting Byzantine robots with a random behavior strategy during collective design-making in swarm robotic systems

The active introduction of robotics swarm systems into life brings the issues of their information security up to date. Known security approaches often do not take into account the peculiarities of the implementation of swarm systems, such as collective design-making, and only consider the presence of Byzantine robots with a strategy of behavior consisting in voting against a majority when a consensus is reached. The aim of this work is to increase the security of the collective design-making process in a swarm robotics system from the imposition of false and ineffective alternatives by Byzantine robots with a random behavior strategy. It is proposed to use an approach based on the use of a distributed ledger and analysis of deviations in the process of collective design-making, which will allow identifying and isolating harmful effects. The solution to the problem of detecting Byzantine robots is based on the application of the criterion of the degree of confidence of a robot in choosing an alternative when a consensus is reached by the swarm system and is based on the assumption that the distribution of the degree of confidence of a Byzantine robot due to ignoring the parameters of the external environment and voting for random alternatives is significantly different from the behavior an ordinary robot. The elements of novelty of the presented solution include the use of the degree of confidence criterion to ensure the safety of collective design-making and the ability to take into account various strategies of behavior of Byzantine robots. The use of the presented solution makes it possible to increase the efficiency of reaching consensus by a swarm robotics system in the presence of Byzantine robots. The simulation for a swarm of 20 robots, including 5 Byzantine ones with random behavior, showed an increase in the probability of correctly reaching a consensus by 12.5%. The practical significance of the presented solutions lies in the possibility of ensuring the stability of reaching consensus by a swarm robotics system in the presence of robots with harmful behavior.