Browsing by Author "Mohammadzadeh, A."

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Open Access
A new deep learning restricted boltzmann machine for energy consumption forecasting
(MDPI, 2022-08) Xu, A.; Tian, M. W.; Firouzi, Behnam; Alattas, K. A.; Mohammadzadeh, A.; Ghaderpour, E.; Firouzi, Behnam
A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.
Metadata only
A new path following scheme: safe distance from obstacles, smooth path, multi-robots
(Springer, 2023-04) Mohammadzadeh, A.; Firouzi, Behnam; Firouzi, Behnam
Robot routing is one of the most important topics in mobile robotics. The goal is to find a continuous path from an initial position to an end destination that is collision-free and optimal or near-optimal. Due to the growing trend of using automatic moving tools in industrial automation, and their application for various purposes such as transportation of goods, and service in industrial and hospital environments, many researchers have decided to conduct research in this field and route planning. The main challenge is to find a short route with a lack of collision with obstacles. This study examines path design for mobile robots and proposes a new and efficient idea for routing. Besides the short distance of the route and lack of collision with obstacles, the proposed method investigates other factors such as the safe distance from obstacles, path smoothness, and multiple robots. The results show the superior precision and speed of the proposed algorithm compared to similar algorithms. The suggested approach finds the shortest path with a safe distance from obstacles, in a minimum time. The major contributions of this method are summarized below: (1) a biogeographical algorithm is formulated for robot routing. (2) To improve the basic biogeographical algorithm, basic operations of the particle swarm optimization and genetic algorithm are integrated with it. (3) In addition to the shortest path problem, other problems such as path smoothness with a new idea and the safe distance from obstacles are included. Path smoothing is performed without involving it in the cost function, and merely through interpolation of the points found by the algorithm. (4) The proposed algorithm results in a good efficiency and finds the appropriate solution in a few iterations.
Open Access
A type-2 fuzzy controller for floating tension-leg platforms in wind turbines
(MDPI, 2022-03) Firouzi, Behnam; Alattas, K. A.; Bakouri, M.; Alanazi, A. K.; Mohammadzadeh, A.; Mobayen, S.; Fekih, A.; Firouzi, Behnam
This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system’s stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.