Electricity consumption forecasting has a main role in the energy supply management system of a power supply company. A power supply company needs to keep the balancing of the electricity demand and supply for their customers. The target is to forecast the electricity consumption in manufacturing company for each 30-minutes in the next day to prevent the lack of electricity supply from a power supply company. Due to this problem, it is the challenge for short term electricity time series consumption forecasting. In this work, we proposed the model of deep learning neural network with approach the combination of Long Short-Term Memory (LSTM) and Feed Forward Neural Network (FFNN) to perform the electricity forecasting. This proposed method (LSTM-FFNN) was implemented in the time-series data of electricity consumption on a manufacturing company. In our experiment, we used LSTM to perform the time-series forecasting by using historical data of electricity consumption, and we performed FFNN along with additional information which represented by one-hot encoding shape to increase the forecasting performance. Experimental results showed that LSTM-FFNN gave the better result as we compared with our baseline which is the original LSTM and Moving Average (MA) based on the Root Mean Squared Error (RMSE) score.

In most Asian countries, rice drying is carried out manually by exposing rice to sunlight. However, problem occurs when rain season comes. Lack of sunlight deters the drying process. This paper proposes a design of mechanical rice drying machine with hybrid sun-biomass energy source. Pahl & Beitz method, which consists of four steps process: function planning and clarification, design concept, design prototype, and design details; are used as design methodology. Based on design result and calculation, in this paper propose specifications for drying machine and biomass furnace. Drying chamber is a continuous flow system with pneumatic-conveyor as blower. This hybrid utilizes two types of energy sources, sun and biomass. The proposed machine has capacity of 500 kilograms per cycle using 455 Watt of energy, which is more efficient than ordinary heater. Biomass furnace utilizes heat transfer by means of arranging 64 pieces of stainless steel pipes of 0.65 diameters in parallel.

Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.