A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

Selection criteria for drought tolerance at the vegetative phase in early maturing maize

Identifying drought tolerant maize (Zea mays L.) at the vegetative stage is a meaningful effort at reducing cost and time of screening large number of maize genotypes for drought tolerance. The primary objectives of this study were to assess the effectiveness of vegetative traits in discriminating between drought tolerant and drought sensitive hybrids and to determine the stage at which the stress should be imposed to achieve maximum difference between hybrids with contrasting responses to drought. A drought tolerant hybrid (TZEI 18 × TZEI 31) and a sensitive hybrid (TZEI 108 × TZEI 87) were evaluated in a pot experiment conducted in a screen house facility and in the field at the Teaching and Research Farm of the Faculty of Agriculture, Obafemi Awolowo University, Ile-Ife in 2011. The experiment was laid out as a randomized complete block design in each of four groups of different water treatments, namely one week of watering for 1, 2, and 3 weeks after planting and withdrawing watering for the rest of the period of experimentation (43 days after planting), along with a treatment involving watering throughout the period of the experiment. Data were collected on root and shoot traits under the four levels of water treatment and the data were subjected to analysis of variance (ANOVA) and orthogonal contrasts. Results of the ANOVA showed significant mean squares for root length, root fresh weight, shoot length, number of root branches, shoot dry weight, root dry weight and number of shed leaves. Withdrawing water a week or two after planting induced large differences between the drought tolerant and drought sensitive genotypes for root length, root dry weight, number of root branches and number of shed leaves. In conclusion, root length, root fresh weight, shoot length, number of root branches, shoot dry weight, root dry weight and number of shed leaves were the most reliable traits for pre-anthesis drought tolerance. Watering for only one or two weeks after planting was the best treatment for identifying drought tolerant maize genotypes at the vegetative growth stage.Key words: Drought, maize, pre-anthesis, seedling stage

Characterization of elite maize inbred lines for drought tolerance using Simple Sequence Repeats markers

The development of drought tolerant maize has been limited by the suggested complexity of the environment on drought phenotypic traits. However, some simple sequence repeats (SSRs) molecular markers linked to drought tolerance via quantitative trait loci (QTL) have been identified in maize but their use requires validation on newly developed elite maize inbred lines. This study therefore aims to validate 19 selected SSR markers linked to maize drought tolerance and determine the genetic diversity of sixty-eight elite maize inbred lines. Genomic DNA was extracted with a CTAB method and the PCR products were separated on agarose gel with auto radiograms visually scored for polymorphic bands to establish a data matrix. Assessment of the genetic links among the inbred lines was carried out using cluster analysis. The 68 maize inbred lines were clustered based on a matrix of genetic similarity Jaccard using the UPGMA algorithm. Some of the markers that were informative included P-bnlg238, Phi037, P-bnlg1179 and Umc2214 and these showed significant group differentiation among the inbred lines. Marker Umc1447, Umc1432 and Umc2359 were among the markers with monomorphic bands, while Phi034, Bnlg1074 and P-umc1542 showed no characterized bands. The polymorphism information content (PIC) value of the informative markers ranged from 0.13 (Bnlg434) to 0.76 (P-bnlg238). The cluster analysis classified the maize inbred lines into four groups based on the SSR data. The exploitation of information of genetic diversity among the inbred maize lines to develop drought tolerant hybrids is hereby discussed

Inheritance of seed quality traits and concentrations of zinc and iron in maize topcross hybrids

Information about the mode of inheritance of maize ( Zea mays L.) seed quality traits is crucial in planning for improvement programmes for such traits. The objective study was to determine mode of inheritance and interrelationships between seed quality traits, and Fe and Zn contents in maize. Twenty-six maize genotypes were considered for evaluation in this study. Additive gene action was prevalent for most seed quality traits (>50%); while non-additive gene action was preponderant for Fe and Zn concentrations. Inbreds TZEEI82 and TZEEI64 were outstanding in terms of GCA male effects for conductivity (-0.13** and -0.06*), root number (0.79** and 0.30*), and root fresh weight (0.90*). Genotypes TZEEI81, DTE-STR-Y-SYN-POP-C3, 2009-TZEEI-OR1-STR and 2009-TZEE-OR1-STR-QPM were identified as excellent pollen parents for Fe concentration; and TZEEI58 and TZEEI64 for Zn concentration. In addition, only germination index had a significant additive genetic relationship with Fe content (r=0.57*); while both shoot fresh and dry weights had significant positive correlations with Zn content (r=0.45*, 0.53*). Overall, it is clear that different modes of gene action control inheritance of seed quality traits and Fe and Zn concentrations.L\u2018 information sur le mode de transmission des caract\ue8res de qualit\ue9 des semences de ma\uefs ( Zea mays L.) est cruciale dans la planification d\u2019un programme d\u2019am\ue9lioration de ces caract\ue8res. L\u2019objectif de cette \ue9tude \ue9tait de d\ue9terminer le mode d\u2019h\ue9r\ue9dit\ue9 et les relations entre les caract\ue8res de qualit\ue9 des semences et les teneurs en Fe et Zn du ma\uefs. Vingt-six g\ue9notypes de ma\uefs ont \ue9t\ue9 \ue9valu\ue9s pour les caract\ue8res de qualit\ue9 des semences ainsi que pour les teneurs en Fe et Zn. L\u2019action des g\ue8nes additifs \ue9tait pr\ue9dominante pour la plupart des caract\ue8res de qualit\ue9 des semences (> 50%); tandis que l\u2019action g\ue9nique non additive \ue9tait pr\ue9pond\ue9rante pour les concentrations de Fe et de Zn. Les consanguines TZEEI82 et TZEEI64 ont \ue9t\ue9 remarquables en termes d\u2019effets GCA m\ue2les pour la conductivit\ue9 (-0,13 ** et -0,06 *), le nombre de racines (0,79 ** et 0,30 *) et le poids des racines fra\ueeches (0,90 *). Les g\ue9notypes TZEEI81, DTE-STR-Y-SYN-POP-C3, 2009-TZEEI-OR1-STR et 2009-TZEE-OR1-STR-QPM ont \ue9t\ue9 identifi\ue9s comme d\u2019excellents parents de pollen pour la concentration de Fe; et TZEEI58 et TZEEI64 pour la concentration de Zn. De plus, seul l\u2019indice de germination avait une relation g\ue9n\ue9tique additive significative avec la teneur en Fe (r = 0,57 *); tandis que les poids frais et secs des pousses avaient des corr\ue9lations positives significatives avec la teneur en Zn (r = 0,45 *, 0,53 *). Dans l\u2019ensemble, il est clair que diff\ue9rents modes d\u2019action g\ue9nique contr\uf4laient l\u2019h\ue9r\ue9dit\ue9 des caract\ue8res de qualit\ue9 des semences et des concentrations de Fe et Zn

A Framework for Electronic Toll Collection in Smart and Connected Communities

Abstract—The number of vehicles plying the highways keeps growing at a steady pace, leading to high maintenance costs. Toll collection was introduced as a means of raising funds for road maintenance, but the traditional method is usually slow and is prone to cause vehicular traffic congestion on the highways. In this paper, a framework was proposed for Electronic Toll Collection (ETC) in smart and connected communities. The main components of the intelligent system architecture are the wireless sensor nodes, web and mobile applications, and a cloud platform. The Wireless Sensor Network (WSN) enables vehicle detection and classification, and establishes a communication link to the back-end of the system. The central database and the web server are hosted in the Cloud while a mobile application is used for electronic transactions, subscription renewal, notification of toll payments, and for tracking toll payment history. In addition, a web dash board is provided for efficient toll administration. The implementation of this system will improve the toll collection efficiency in terms of speed and flexibility. Overall, the contribution of this work extends the frontier of WSNs to the domain of Intelligent Transportation System (ITS)

Grain yield of maize varieties of different maturity groups under marginal rainfall conditions

This study was conducted to evaluate the yield performance of different maturity groups of maize varieties at different planting dates under the marginal rainfall conditions of the rainforest ecology of Nigeria and identify the high yielding ones. The maize varieties were evaluated on five and three different planting dates in 2001 and 2005 late cropping seasons respectively. Seven planting dates were used in 2002 and 2006 early cropping seasons. All plantings were done at a weekly interval. Data were obtained on grain yield and yield components. Grain yield and yield components decreased as planting was delayed in the late seasons while in the early seasons they showed contrasting trend. To obtain optimum yield for the maturity classes evaluated, the varieties must be planted about the end of August or first week of September for the late season and about the middle of April in the early season. At the optimum planting date TZEE- WSRBCs and ACR 90 POOL16-DT with grain yield of 3.8 tons ha-1 and 6.4 tons ha-1 were the highest yielding varieties in 2001 and 2002 respectively. In 2005 late cropping season, TZECOMP3DT (1.7 tons/ha) was the highest yielding while in 2006 early cropping seasons, ACR 95 TZECOMP4C3 (4.37 tons/ha) was the highest yielding variety

Grain yield of maize varieties of different maturity groups under marginal rainfall conditions

This study was conducted to evaluate the yield performance of different maturity groups of maize varieties at different planting dates under the marginal rainfall conditions of the rainforest ecology of Nigeria and identify the high yielding ones. The maize varieties were evaluated on five and three different planting dates in 2001 and 2005 late cropping seasons respectively. Seven planting dates were used in 2002 and 2006 early cropping seasons. All plantings were done at a weekly interval. Data were obtained on grain yield and yield components. Grain yield and yield components decreased as planting was delayed in the late seasons while in the early seasons they showed contrasting trend. To obtain optimum yield for the maturity classes evaluated, the varieties must be planted about the end of August or first week of September for the late season and about the middle of April in the early season. At the optimum planting date TZEE- WSRBCs and ACR 90 POOL16-DT with grain yield of 3.8 tons ha-1 and 6.4 tons ha-1 were the highest yielding varieties in 2001 and 2002 respectively. In 2005 late cropping season, TZECOMP3DT (1.7 tonsha) was the highest yielding while in 2006 early cropping seasons, ACR 95 TZECOMP4C3 (4.37 tonsha) was the highest yielding variety

A deep learning model for electricity demand forecasting based on a tropical data

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists

Published Online: Sep 06, 2014Genotype main effect plus genotype-by-environment interaction (GGE) biplot produces a graphical display of results that facilitates a better understanding of complex genotype-by-environment interaction in multi-environment trials of breeding and agronomic experiments. However, the full potential and weaknesses of this powerful tool are not fully understood by breeders, agronomists, entomologists and pathologists. The objective of this paper was to review the usefulness of this statistical tool and enumerate some of its weaknesses. Its main application has so far been in the analysis of multi-environment data. It has been used to analyze the performance of crop cultivars under multiple stress environments, from which ideal cultivars, mega-environments, and core testing sites were identified. More recently, GGE biplot has been employed in genetic analysis of diallel data to estimate the combining abilities and identify heterotic groups among inbred parents. Genotype-by-trait biplot has also been utilized in trait profile analysis, and in identification of traits that are reliable for indirect selection of a target primary trait. Two major shortcomings of this tool are (i) failure to identify more than two distinct, contrasting groups in diallel studies and (ii) lack of statistical tests for most of its graphical displays. Other aspects of GGE biplot that need further study and development are (i) estimation of genetic variances, covariances, and heritability, including the analysis of data generated from North Carolina Designs I, II, and III as well as other genetic designs, considering their importance in plant breeding programs; (ii) analysis of Quantitative Trait Loci (QTL) data for proper understanding of the genetic constitution of each individual plant or line; and (iii) analysis of Genotype-by-pathogen or insect strain interaction data. Nevertheless, GGE biplot has helped greatly in the accurate analysis and interpretation of data from breeding and agronomic field evaluation experiments