The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

Forecasting electricity load demand: analysis of the 2001 rationing period in Brazil

This paper studies the electricity load demand behavior during the 2001 rationing period, which was implemented because of the Brazilian energetic crisis. The hourly data refers to a utility situated in the southeast of the country. We use the model proposed by Soares and Souza (2003), making use of generalized long memory to model the seasonal behavior of the load. The rationing period is shown to have imposed a structural break in the series, decreasing the load at about 20%. Even so, the forecast accuracy is decreased only marginally, and the forecasts rapidly readapt to the new situation. The forecast errors from this model also permit verifying the public response to pieces of information released regarding the crisis.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Load Profile Analysis Tool for Electrical Appliances in Households

This paper presents a methodology to forecast the hourly and daily consumption in households. The methodology was validated for households in Lisbon region, Portugal. The paper shows that the forecast tool allows obtaining satisfactory results for forecasting. Models of demand response allow the support of consumer’s decision in exchange for an economic benefit by the redefinition of load profile or changing the appliance consumption period. It is also in the interest of electric utilities to take advantage of these changes, particularly when consumers have an action on the demand-side management or production. Producers need to understand the load profile of households that are connected to a smart grid, to promote a better use of energy, as well as optimize the use of micro-generation from renewable sources, not only to delivering to the network but also in self-consumption.

Load Profile Analysis Tool for Electrical Appliances in Households Assisted by CPS

This paper presents a methodology to forecast the hourly and daily consumption in households assisted by cyber physical systems. The methodology was validated using a database of consumption of a set of 93 domestic consumers. Forecast tools used were based on Fast Fourier Series and Generalized Reduced Gradient. Both tools were tested and their forecast results were compared. The paper shows that both tools allow obtaining satisfactory results for energy consumption forecasting.

Effect of cyclic load on vertical misfit of prefabricated and cast implant single abutment

OBJECTIVES: The purpose of this in vitro study was to evaluate misfit alterations at the implant/abutment interface of external and internal connection implant systems when subjected to cyclic loading. MATERIAL AND METHODS: Standard metal crowns were fabricated for 5 groups (n=10) of implant/abutment assemblies: Group 1, external hexagon implant and UCLA cast-on premachined abutment; Group 2, internal hexagon implant and premachined abutment; Group 3, internal octagon implant and prefabricated abutment; Group 4, external hexagon implant and UCLA cast-on premachined abutment; and Group 5, external hexagon implant and Ceraone abutment. For groups 1, 2, 3 and 5, the crowns were cemented on the abutments and in group 4 crowns were screwed directly on the implant. The specimens were subjected to 500,000 cycles at 19.1 Hz of frequency and non-axial load of 133 N in a MTS 810 machine. The vertical misfit (μm) at the implant/abutment interface was evaluated before (B) and after (A) application of the cyclic loading. Data were analyzed statistically by using two-away ANOVA and Tukey's post-hoc test (p<0.05). RESULTS: Before loading values showed no difference among groups 2 (4.33±3.13), 3 (4.79±3.43) and 5 (3.86±4.60); between groups 1 (12.88±6.43) and 4 (9.67±3.08), and among groups 2, 3 and 4. However, groups 1 and 4 were significantly different from groups 2, 3 and 5. After loading values of groups 1 (17.28±8.77) and 4 (17.78±10.99) were significantly different from those of groups 2 (4.83±4.50), 3 (8.07±4.31) and 5 (3.81±4.84). There was a significant increase in misfit values of groups 1, 3 and 4 after cyclic loading, but not for groups 2 and 5. CONCLUSIONS: The cyclic loading and type of implant/abutment connection may develop a role on the vertical misfit at the implant/abutment interface.

Load Ratio Estimation Through Striation Height and Spacing Analysis of an Aerospace Al Alloy 7475-T7351

It is well known that striation spacing may be related to the crack growth rate, da/dN, through Paris equation, as well as the maximum and minimum loads under service loading conditions. These loads define the load ratio, R, and are considered impossible to be evaluated from the inter-spacing striations analysis. In this way, this study discusses the methodology proposed by Furukawa to evaluate the maximum and minimum loads based on the experimental fact that the relative height of a striation, H, and the striation spacing, s, are strongly influenced by the load ratio, R. Fatigue tests in C(T) specimens were conducted on SAE 7475-T7351 Al alloy plates at room temperature and the results showed a straightforward correlation between the parameters H, s, and R. Measurements of striation height, H, were performed using scanning electron microscopy and field emission gun (FEG) after sectioning the specimen at a large inclined angle to amplify the height of the striations. The results showed that for increasing R the values of H/s tend to increase. Striation height, striation spacing, and load ratio correlations were obtained, which allows one to estimate service loadings from fatigue fracture surface survey.

Methodology to forecast the harvest date of banana bunches

Introduction. This method is used to forecast the harvest date of banana bunches from as early as the plant shooting stage. It facilitates the harvest of bunches with the same physiological age. The principle, key advantages, time required and expected results are presented. Materials and methods. Details of the four steps of the method ( installation of the temperature sensor, tagging bunches at the flowering stage, temperature sum calculation and estimation of bunch harvest date) are described. Possible problems are discussed. Results. The application of the method allows drawing a curve of the temperature sum accumulated by the bunches which have to be harvested at exactly 900 degree-days physiological age.

The frequency of CD127(low) expressing CD4(+)CD25(high) T regulatory cells is inversely correlated with human T lymphotrophic virus type-1 (HTLV-1) proviral load in HTLV-1-infection and HTLV-1-associated myelopathy/tropical spastic paraparesis

Background: CD4(+)CD25(high) regulatory T (T(Reg)) cells modulate antigen-specific T cell responses, and can suppress anti-viral immunity. In HTLV-1 infection, a selective decrease in the function of T(Reg) cell mediated HTLV-1-tax inhibition of FOXP3 expression has been described. The purpose of this study was to assess the frequency and phenotype of T(Reg) cells in HTLV-1 asymptomatic carriers and in HTLV-1-associated neurological disease (HAM/TSP) patients, and to correlate with measures of T cell activation. Results: We were able to confirm that HTLV-1 drives activation, spontaneous IFN gamma production, and proliferation of CD4+ T cells. We also observed a significantly lower proportion of CTLA-4(+) T(Reg) cells (CD4(+)CD25(high) T cells) in subjects with HAM/TSP patients compared to healthy controls. Ki-67 expression was negatively correlated to the frequency of CTLA-4(+) T(Reg) cells in HAM/TSP only, although Ki-67 expression was inversely correlated with the percentage of CD127(low) T(Reg) cells in healthy control subjects. Finally, the proportion of CD127(low) T(Reg) cells correlated inversely with HTLV-1 proviral load. Conclusion: Taken together, the results suggest that T(Reg) cells may be subverted in HAM/TSP patients, which could explain the marked cellular activation, spontaneous cytokine production, and proliferation of CD4(+) T cells, in particular those expressing the CD25(high)CD127(low) phenotype. T(Reg) cells represent a potential target for therapeutic intervention for patients with HTLV-1-related neurological diseases.