Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability (
R
2
) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the
auto regressive integrated moving average (ARIMA)
technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.
In the current scenario of Wireless Sensor Network (WSN), power consumption is the major issue associated with nodes in WSN. LEACH technique plays a vital role of clustering in WSN and reduces the energy usage effectively. But LEACH has its own limitation in order to search cluster head nodes which are randomly distributed over the network. In this paper, ERA-NFL- BA algorithm is being proposed for selects the cluster heads in WSN. This algorithm help in selection of cluster heads can freely transform from global search to local search. At the end, a comparison has been done with earlier researcher using protocol ERA-NFL, which clearly shown that proposed Algorithm is best suited and from comparison results that ERA-NFL-BA has given better performance.
IN an assessment such as this, one could cover a wide range of topics fairly shallowly or a lesser number in a bit more depth. I have opted for the latter. The topics discussed will embrace some trends in dairying, beef farming, sheep farming, hill country farming, and land use generally, species and variety usage in grassland farming, use of crude protein produced from pasture, and some implications of energy usage.
In this paper, the design and application of smart wheelchair and charging station for disabled citizen is realized. The first stage of the paper is to make the wheelchair used by our disabled citizens able to access smart home technology via the vehicle via touch screen. The ability of citizens with disabilities to call with direct access via touch screen is also in the wheelchair designed. Thanks to the touch screen placed on the vehicle, disabled citizens are provided with the control of smart automation to control many objects such as curtains and doors in the home. In the second part of the paper, a solar powered charging station is designed and installed in order to charge battery powered wheelchairs. In the charging station made a special card reader system and has the charger to charge the card with disabilities to actively and means are provided.
A novel Smart Home Energy Management system: Cooperative neighbourhood and adaptive renewable energy usage
