Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

Voltage Analysis and Load Curtailment Minimization in Sub-transmission Networks with Distributed Generation

Most of distributed generation and smart grid research works are dedicated to network operation parameters studies, reliability, etc. However, many of these works normally uses traditional test systems, for instance, IEEE test systems. This paper proposes voltage magnitude and reliability studies in presence of fault conditions, considering realistic conditions found in countries like Brazil. The methodology considers a hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models and a remedial action algorithm which is based on optimal power flow. To illustrate the application of the proposed method, the paper includes a case study that considers a real 12-bus sub-transmission network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Adsorption behavior of α-cypermethrin on cork and activated carbon

Studies were undertaken to determine the adsorption behavior of α-cypermethrin [R)-α-cyano-3-phenoxybenzyl(1S)-cis- 3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylate, and (S)-α-cyano-3-phenoxybenzyl (1R)-cis-3-(2,2-dichlorovinyl)-2,2- dimethylcyclopropanecarboxylate] in solutions on granules of cork and activated carbon (GAC). The adsorption studies were carried out using a batch equilibrium technique. A gas chromatograph with an electron capture detector (GC-ECD) was used to analyze α-cypermethrin after solid phase extraction with C18 disks. Physical properties including real density, pore volume, surface area and pore diameter of cork were evaluated by mercury porosimetry. Characterization of cork particles showed variations thereby indicating the highly heterogeneous structure of the material. The average surface area of cork particles was lower than that of GAC. Kinetics adsorption studies allowed the determination of the equilibrium time—24 hours for both cork (1–2 mm and 3–4 mm) and GAC. For the studied α-cypermethrin concentration range, GAC revealed to be a better sorbent. However, adsorption parameters for equilibrium concentrations, obtained through the Langmuir and Freundlich models, showed that granulated cork 1–2 mm have the maximum amount of adsorbed α-cypermethrin (qm) (303 μg/g); followed by GAC (186 μg/g) and cork 3-4 mm (136 μg/g). The standard deviation (SD) values, demonstrate that Freundlich model better describes the α-cypermethrin adsorption phenomena on GAC, while α-cypermethrin adsorption on cork (1-2 mm and 3-4 mm) is better described by the Langmuir. In view of the adsorption results obtained in this study it appears that granulated cork may be a better and a cheaper alternative to GAC for removing α-cypermethrin from water.

A lightweight indoor localization model based on motley-keenan and COST

This paper presents a novel approach to WLAN propagation models for use in indoor localization. The major goal of this work is to eliminate the need for in situ data collection to generate the Fingerprinting map, instead, it is generated by using analytical propagation models such as: COST Multi-Wall, COST 231 average wall and Motley- Keenan. As Location Estimation Algorithms kNN (K-Nearest Neighbour) and WkNN (Weighted K-Nearest Neighbour) were used to determine the accuracy of the proposed technique. This work is based on analytical and measurement tools to determine which path loss propagation models are better for location estimation applications, based on Receive Signal Strength Indicator (RSSI).This study presents different proposals for choosing the most appropriate values for the models parameters, like obstacles attenuation and coefficients. Some adjustments to these models, particularly to Motley-Keenan, considering the thickness of walls, are proposed. The best found solution is based on the adjusted Motley-Keenan and COST models that allows to obtain the propagation loss estimation for several environments.Results obtained from two testing scenarios showed the reliability of the adjustments, providing smaller errors in the measured values values in comparison with the predicted values.