In recent years, the issue of energy consumption modeling techniques in the building sector has been widely considered by researchers and managers. Researches indicate that energy consumption in residential sector is one of the main parts of the total consumption. Moreover, the urban residence is supposed as a significant consumer of energy in every country and therefore a focus for energy consumption efforts. For over increasing building energy consumption there can be some causes including: climate change, increase in household electricity load, fast-growing household electrical appliances, huge energy consumption of the buildings, and the lack of strict government supervision. Recently the issue of assigning different prices to different time-periods of energy consumption has been taken into consideration for energy prices of consumers’ bills. Therefore, in this paper a model for multi-prices and multi-periods single-type energy consumption in urban residential sector is presented; also an efficient heuristic method is provided for solving the mentioned model.

The economic and demographic development of Morocco in the last few years has led to significant residential energy consumption (REC). Thus, to maintain this development, energy planning should be carried out in a comprehensive and precise manner. Using data from 1990– 2013, two sets of models have been developed to forecast Morocco’ s residential energy demand and their results were compared: one used a multiple linear regression (MLR) technique and the other has relied on a correlative approach. The findings show that correlative models based on a link of REC to GDP and urbanization rate outperforms MLR models, providing more reliable and accurate results in terms of prediction errors during the test period. Forecasted results show that Moroccan residential energy needs will increase by 70%. In 2030, Morocco will consume around 6670 ktoe of energy in residential sector, more precisely, 1937, 4501 and 233 ktoe of electricity, butane and biomass, respectively. In fact, electricity is the final energy which will have the higher increase between 2016 and 2030 (approximately 115%).

Wireless sensors networks consist of several tiny battery powered devices which are utilized to monitor and gather information of desired area, such as a Net Zero Energy Building. As there is no way to recharge the nodes’ energy and batteries, efficient energy consumption plays crucial role in prolonging nodes’ and network’ s lifetime. One of the most popular routing protocol is LEACH protocol, which suffers from drawbacks, despite several modifications have been deployed. This research aims to optimize the Leach protocol from three different aspects. Initially, the allocated energy of network is distributed to each nodes based on their distance with base station. The more distance between a node and BS, the more energy portion is allocated to the given node. Secondly, Genetic algorithm has been employed to select optimal cluster head set whose fitness function addresses number of CHs and distance between CHs and the BS. Lastly, sensor nodes are clustered by fuzzy clustering. Comparing the results with previous versions illustrates that the proposed modifications could prolong considering network and improve the energy consumption and the modified LEACH achieves 10% improvement in terms of survival rate of nodes.