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Prediction of the groundwater quality index through machine learning in Western Middle Chelif plain in North Algeria

Elmeddahi Yamina, Ragab Ragab

Acta Geophysica

2022

Vol. 70, no 4

1797--1814

Water quality monitoring and assessment has been one of the world’s major concerns in recent decades. This study examines the performance of three approaches based on the integration of machine learning and feature extraction techniques to improve water quality prediction in the Western Middle Chelif plain in Algeria during 2014–2018. The most dominant Water Quality Index parameters that were extracted by neuro-sensitivity analysis (NSA) and principal component analysis (PCA) techniques were used in the multilayer perceptron neural network, support vector regression (SVR) and decision tree regression models. Various combinations of input data were studied and evaluated in terms of prediction performance, using statistical criteria and graphical comparisons. According to the results, the MLPNN1 model with eight input parameters gave the highest performance for both training and validation phases (R=0.98/0.95, NSE=0.96/0.88, RMSE=11.20/15.03, MAE=7.89/10.22 and GA=1.34) when compared with the multiple linear regression, TDR and SVR models. Generally, the prediction performance of models integrated with NSA approaches is significantly improved and outperforms models coupled with the PCA dimensionality reduction method.

Modeling and scheduling home appliances using nature inspired algorithms for demand response purpose

Haroun Isra, Shareef Hussain, Ibrahim Ahmad Asrul, Khalid Saifulnizam, Idris Abdelrahman Omer

Przegląd Elektrotechniczny

2021

R. 97, nr 4

60--66

Demand response (DR) refers to programs used in endeavors to reduce overall power consumption, manage consumption peak hour shifting, and reduce demand on service providers or utilities using different methods. This paper proposes a home appliance scheduler suitable for DR applications. In the proposed method, a controller controls thermal and shiftable loads, where thermal loads are empirical models that consider different factors. They produce the load profile of the home in consideration of different input parameters, e.g., setpoints and user tolerance ranges, and various factors, e.g., the room’s physical structure and the external environment. A scheduler uses the controller to implement load shifting using the whale optimization algorithm, particle swarm optimization, and gray wolf optimization (GWO) algorithms for three different occupancy and price schemes. Acceptable results were obtained by applying the models using various outer temperatures and user tolerance ranges. The results also demonstrate cost reduction of 38.59% with GWO for the first occupancy scheme.

Demand Response (DR) oznacza programy do redukcji poboru mocy, doboru czasu pracy, odbiorników energii elektrycznej. W artykule zaproponowano program użycia urządzeń domowych spełniający wymagania DR z uwzględnieniem termicznych warunków pracy. . Zaproponowano algorytmy optymalizacji.