Search

Prediction of electricity demand is made using Load forecasting technique to meet the ever-growing demand. In this paper, future electricity demand forecasted for the whole state of Uttar Pradesh (India), using the dataset collected from the Central Electricity Authority. This dataset consists of electricity demand for the whole state of UP for every 15-min block. Different models were used to forecast future demand. XGradientBoost (XGBoost), a machine learning algorithm was used to forecast demand first. Further forecasting was performed using deep learning models such as Long Short-Term Memory (LSTM) using neural networks as they are considered to be more efficient and accurate than XGBoost. Fuzzy time series (FTS) models were considered to incorporate trend and seasonality present in our dataset. From various FTS models, the best mean absolute percentage error achieved (MAPE) was 2.34%. A new method KmFuzz is proposed in this paper that uses modified K-Means clustering for finding an optimal number of partitions on which fuzzy logic is applied. Fuzzy sets are obtained by applying fuzzification on the dataset and the total number of sets generated are equal to the number of optimal partitions. Then the weighted average method is used for defuzzification and forecasting the next hour demand using the demand data of previous hours with MAPE of 1.94%, thus improving the accuracy further. [ABSTRACT FROM AUTHOR]

Accident prediction is an important safety issue to raise alarms before accidents happen. In this paper we formulate relevant questions to anticipate the occurrence of accidents and process the available information using Hadoop. We examine the execution time on Hadoop when compared with other methods, and propose 2 algorithms to use Congestion Control Machine Framework (CCMF) and Traffic Congestion Analyzer using Map Reduce (TCAMP) to effectively analyze the available data and assess road accident reasons and advise authorities to take appropriate actions to increase road safety. We apply information mining to examine recorded road attributes to reduce road accidents specifically in India, and formulate a set of standards that can be utilized by the National Highway Authority of India to improve safety. [ABSTRACT FROM AUTHOR]

In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. Here we have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The decisive factors for subject assignment may be depth of knowledge, sincerity, class management, contribution towards research, text book publication. Further preference of the faculty member should be considered with top priority unless there are some valid constraints. Again the teaching personnel in a department hold different designations and different administrative responsibility, therefore each of them cannot be assigned equal hours of teaching load. The All India Council for Technical Education (AICTE) guidelines is considered as a baseline for assignment of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization (MOPSO) is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

The hydrological interdependence of plants in cascaded hydroelectric system means that operation of any plant has an effect on water levels and storage at other plants in the system. Hydro-logically efficient operation of power plants in such cascaded system requires that water resources should be managed efficiently, so that it can dispatched to predicted demand considering all physical and operational constraints. Meta-heuristic optimization techniques particularly Particle Swarm Optimization (PSO) and its variants have been successfully used to solve such problem. In this paper Time Varying Acceleration coefficients PSO (TVAC_PSO) has been used to determine the optimal generation schedule of real operated cascaded hydroelectric system located at Narmada river in state Madhya Pradesh, India. Results thus obtained from TVAC_PSO are compared with Novel Self Adaptive Inertia Weight PSO (NSAIW_PSO) and found to give better solution. [ABSTRACT FROM AUTHOR]

Monitoring the Earth's surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth's surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth's surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. [ABSTRACT FROM AUTHOR]

Our research evaluates an innovative sampling technique for household surveys called "geosampling" which leverages recent advances in geographic information systems, computer vision algorithms, and satellite imagery. We compare geosampling to the random walk method. We conducted two surveys in Uttar Pradesh, India: one using geosampling (1,026 completes) and another using random walk (939 completes). We compare the two sampling techniques along three dimensions: (a) performance indicators—response rates and contact attempts; (b) sample composition; and (c) components of variance. We help researchers understand the survey contexts for which geosampling and random walk are best suited. [ABSTRACT FROM AUTHOR]

A detailed regional drought study is carried out in the southern peninsula of India to characterize the spatio-temporal nature of droughts and to predict the drought magnitudes for various probabilities in the homogeneous drought regions. The method of several random initializations of the cluster centres of the K-means algorithm is suggested for the identification of the initial regions in the context of drought regionalization, which is shown to perform better than the initialization from the Ward's algorithm and the Ward's algorithm itself. The peninsula is classified into seven spatially well-separated homogeneous drought regions. The robust L-moment framework is used for the regional frequency analysis of drought magnitudes computed using the standardized precipitation index. The Pearson type III is found to be appropriate for regional drought frequency analysis in six of the regions, while the robust Wakeby distribution is suggested for one region. Low magnitude droughts are frequent and dominant in the northern part of west coast, the north-eastern coast and its adjoining inland region, while high magnitude droughts are less in number and are experienced in semi-arid central part, southern part of western coast, south-eastern part and north-western inland region. The spatial maps of drought magnitudes indicate that at higher return periods (100 and 200 years) the south-eastern part of the peninsula is likely to encounter high magnitude droughts, while the central region is likely to experience the same at lower return periods (10 and 50 years). Hence these regions need to be given special importance in the drought mitigation planning activities. [ABSTRACT FROM AUTHOR]