Scheduling of industrial activities based on weather forecasting and electrical price

Energy is a vital element for growth and development, but the world's energy consumption rates are expected to increase by 33% from 2010 to 2030, making the energy crisis one of the 21st century's most pressing challenges to sustainable human development. One of the most sustainable and effective ways to overcome this problem is by improving efficiency in the commercial, residential and industrial sectors, leading to major interest in finding ways to utilize energy more efficiently in these sectors. Particularly, an HVAC system installed in industrial buildings account for a major portion of energy consumption for maintaining working conditions in terms of temperature, pressure, indoor air quality for indoor activities. The maintenance of working conditions is influenced by a wide variety of factors like building occupants (including machine and human beings), peak/off-peak tariff of electricity and weather conditions. The cost of consumed energy by an HVAC system can be optimized either by using efficient mechanical components or optimizing the schedule of indoor activities on the basis of influencing factors. Research work presented in this thesis involves analysis of computational aspects for rescheduling the indoor activities of an industrial building on the basis of influencing factors. The thesis mainly focuses on rescheduling the indoor industrial activities on the basis of its occupants, peak/off-peak tariff and weather condition for optimizing power consumption and hence minimizing cost of energy consumption in terms of electricity prices. Firstly, prior research has found that heavy industries like steel manufacturing, cement production, oil refinery etc. involve various human and machine activities for producing their goods. This research demonstrated scheduling of various activities within an industrial setup helps to optimize cost for production of items based on electricity prices. However, such studies focused on optimization of electricity cost for only production of products but ignored ambient environment for human beings in the industrial setup. This work proposed a novel model for scheduling of industrial activities for energy consumption of HVAC systems based on different activity groups and solved using Binary Integer Linear Programming with an objective of minimizing HVAC cost. This work contributes by presenting a model for cost optimization of HVAC systems by maintaining ambient environment for the occupants of industrial buildings which was not addressed in prior research. The second contribution involves the refinement to the model for rescheduling the indoor industrial activities by considering impact of weather conditions and peak/off peak tariff on optimizing the cost of energy consumption. The simulation results of the refined model indicates a reduction of 38% in cost of energy consumption of an HVAC system for different time slots. A final novel contribution presented in this work is modifying proposed activity scheduling model by considering real time pricing of New York and Chicago as well as considering weather conditions for both industrial cities. The proposed model is validated using a benchmark dataset of indoor industrial activities and year-round cost of energy consumed. The simulation results demonstrate an overall accuracy of 85% of the predicted cost values in comparison to actual values of cost of energy consumed by an HVAC system in terms of electricity prices. The proposed model ensures an annual saving in electricity prices up to 29%. Hence, the proposed model is an effective approach for optimizing energy consumption by rescheduling the indoor industrial activities based upon weather conditions and cost tariff throughout the year. In summary the work presented in this thesis provides a comprehensive step to optimize the energy usage and hence operational cost of HVAC systems by carefully rescheduling indoor activities of an industrial building.