Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.
The exponential growth of mobile data traffic and a limited number of spectrum resources has been a big challenge for cellular network providers, henceforth traffic offloading has become one of the most critical issues especially in 5G Heterogeneous Networks (HetNets). Further, network selection plays a vital role for traffic offloading in a cellular network to maintain Quality of Service (QoS), increasing offloading efficiency and throughput. In order to efficiently utilize spectral resources, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm is proposed to be used for ranking a candidate network. The proposed algorithm helps in alleviating the spectrum shortage by offloading the data traffic over Wi-Fi network using unlicensed spectrum. In this work, analysis of the performance of the proposed system model through simulation of an analytical framework has been made. The results have been accumulated in terms of cumulative handover, throughput, the extent of equilibrium & offloading efficiency with respect to residence time and the number of Wi-Fi Access Points (AP's). Analysis proves that the proposed algorithm improves the equilibrium extent and throughput as compared to traditional Load balancing (LB) and SDN based LB mechanisms. It also shows that offloading efficiency is highly improved over Wi-Fi density and residence time.
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