Supervised electricity tariff prediction using random forest validated through user comfort and constraint for a home energy management scheme

2021 ◽  
Vol 40 (1) ◽  
pp. 745-757
Author(s):  
Ganesh Kumar Chellamani ◽  
M. Firdouse Ali Khan ◽  
Premanand Venkatesh Chandramani
Keyword(s):  
Random Forest ◽  
Energy Management ◽  
Performance Metrics ◽  
Support Vector ◽  
End User ◽  
Short Term ◽  
Management Scheme ◽  
Home Energy Management ◽  
Electricity Tariff ◽  
User Comfort

Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ...  , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort.

Naïve Bayes classifier enabled home energy management scheme for cost-effective end-user comfort

2021 ◽  
Vol 40 (1) ◽  
pp. 403-413
Author(s):  
M. Firdouse Ali Khan ◽  
Ganesh Kumar Chellamani ◽  
Premanand Venkatesh Chandramani
Keyword(s):  
Energy Management ◽  
Time Window ◽  
Cost Effective ◽  
Pattern Generation ◽  
Home Appliances ◽  
Management Scheme ◽  
Home Energy Management ◽  
Maximum Comfort ◽  
The Cost ◽  
User Comfort

Under demand response enabled demand-side management, the home energy management (HEM) schemes schedule appliances for balancing both energy and demand within a residence. This scheme enables the user to achieve either a minimum electricity bill (EB) or maximum comfort. There is always the added burden on a HEM scheme to obtain the least possible EB with comfort. However, if a time window that contains comfortable time slots of the day for an appliance operation, is identified, and if the cost-effective schedule-pattern gets generated from these windows autonomously, then the burden can be reduced. Therefore, this paper proposes a two-level method that can assist the HEM scheme by generating a cost-effective schedule-pattern for scheduling home appliances. The first level uses a classifier to identify the comfortable time window from past ON and OFF events. The second level uses pattern generation algorithms to generate a cost-effective schedule-pattern from the identified window. The generated cost-effective schedule-pattern is applied to a HEM scheme as input to demonstrate the proposed two-level approach. The simulation results exhibit that the proposed approach helps the HEM scheme to schedule home appliances cost-effectively with a satisfactory user-comfort between 90% and 100%.

scholarly journals Home Energy Management System Incorporating Heat Pump Using Real Measured Data

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2937 ◽  
Author(s):  
Cao ◽  
O’Rourke ◽  
Lyons ◽  
Han
Keyword(s):  
Energy Management ◽  
Heat Pump ◽  
Measured Data ◽  
Management Systems ◽  
End User ◽  
Energy Management Systems ◽  
Comfort Levels ◽  
Home Energy Management ◽  
The Cost ◽  
User Comfort

The demand for electricity has been rising significantly over the past years and it is expected to rise further in the coming years due to economic and societal development. Smart grid technology is being developed in order to meet the rising electricity requirement. In order for the smart grid to perform its full functions, the Energy Management Systems (EMSs), especially Home Energy Management Systems (HEMS) are essential. It is necessary to understand the energy demand of the loads and the energy supply either from the national grid or from renewable energy technologies. To facilitate the Demand Side Management (DSM), Heat Pumps (HP) and air conditioning systems are often utilised for heating and cooling in residential houses due to their high-efficiency power output and low CO2 emissions. This paper presents a program for a HEMS using a Particle Swarm Optimisation (PSO) algorithm. A HP is used as the load and the aim of the optimisation program is to minimise the operational cost, i.e., the cost of electricity, while maintaining end-user comfort levels. This paper also details an indoor thermal model for temperature update in the heat pump control program. Real measured data from the UK Government’s Renewable Heat Premium Payment (RHPP) scheme was utilised to generate characteristic curves and equations that can represent the data. This paper compares different PSO variants with standard PSO and the unscheduled case calculated from the data for five winter days in 2019. Among all chosen algorithms, the Crossover Subswarm PSO (CSPSO) achieved an average saving of 25.61% compared with the cost calculated from the measured data with a short search time of 1576 ms for each subswarm. It is clear from this work that there is significant scope to reduce the cost of operating a HP while maintaining end user comfort levels.

scholarly journals QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2304 ◽  
Author(s):  
Mingfu Li ◽  
Guan-Yi Li ◽  
Hou-Ren Chen ◽  
Cheng-Wei Jiang
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Smart Home ◽  
Control Algorithm ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Electricity Bill ◽  
Home Energy Management ◽  
Smart Appliance ◽  
User Comfort

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

Determinants of rental strategy: short-term vs long-term rental strategy

2020 ◽  
Vol 13 (12) ◽  
pp. 3873-3894
Author(s):  
Sina Shokoohyar ◽  
Ahmad Sobhani ◽  
Anae Sobhani
Keyword(s):  
Nearest Neighbor ◽  
Performance Metrics ◽  
Sharing Economy ◽  
Real Estate Market ◽  
Support Vector ◽  
Attractive Alternative ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Content Type

Purpose Short-term rental option enabled via accommodation sharing platforms is an attractive alternative to conventional long-term rental. The purpose of this study is to compare rental strategies (short-term vs long-term) and explore the main determinants for strategy selection. Design/methodology/approach Using logistic regression, this study predicts the rental strategy with the highest rate of return for a given property in the City of Philadelphia. The modeling result is then compared with the applied machine learning methods, including random forest, k-nearest neighbor, support vector machine, naïve Bayes and neural networks. The best model is finally selected based on different performance metrics that determine the prediction strength of underlying models. Findings By analyzing 2,163 properties, the results show that properties with more bedrooms, closer to the historic attractions, in neighborhoods with lower minority rates and higher nightlife vibe are more likely to have a higher return if they are rented out through short-term rental contract. Additionally, the property location is found out to have a significant impact on the selection of the rental strategy, which emphasizes the widely known term of “location, location, location” in the real estate market. Originality/value The findings of this study contribute to the literature by determining the neighborhood and property characteristics that make a property more suitable for the short-term rental vs the long-term one. This contribution is extremely important as it facilitates differentiating the short-term rentals from the long-term rentals and would help better understanding the supply-side in the sharing economy-based accommodation market.

scholarly journals Development of Home Energy Management Scheme for a Smart Grid Community

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4288 ◽  
Author(s):  
Md Mamun Ur Rashid ◽  
Fabrizio Granelli ◽  
Md. Alamgir Hossain ◽  
Md. Shafiul Alam ◽  
Fahad Saleh Al-Ismail ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Operating Time ◽  
Power Sharing ◽  
Electricity Cost ◽  
Home Appliances ◽  
Optimal Power ◽  
Management Scheme ◽  
Home Energy Management

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

scholarly journals IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2802 ◽  
Author(s):  
Qurat-ul Ain ◽  
Sohail Iqbal ◽  
Safdar Khan ◽  
Asad Malik ◽  
Iftikhar Ahmad ◽  
...  
Keyword(s):  
Operating System ◽  
Energy Consumption ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Inference System ◽  
Intelligent Sensors ◽  
Home Energy Management ◽  
User Comfort

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

scholarly journals Machine Learning Framework to Predict Chronic Kidney Disease using Ensemble Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 1-6
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Performance Metrics ◽  
Weighted Average ◽  
Gradient Boosting ◽  
Support Vector ◽  
The Individual

Chronic Kidney Disease (CKD) is a worldwide concern that influences roughly 10% of the grown-up population on the world. For most of the people the early diagnosis of CKD is often not possible. Therefore, the utilization of present-day Computer aided supported strategies is important to help the conventional CKD finding framework to be progressively effective and precise. In this project, six modern machine learning techniques namely Multilayer Perceptron Neural Network, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Logistic regression were used and then to enhance the performance of the model Ensemble Algorithms such as ADABoost, Gradient Boosting, Random Forest, Majority Voting, Bagging and Weighted Average were used on the Chronic Kidney Disease dataset from the UCI Repository. The model was tuned finely to get the best hyper parameters to train the model. The performance metrics used to evaluate the model was measured using Accuracy, Precision, Recall, F1-score, Mathew`s Correlation Coefficient and ROC-AUC curve. The experiment was first performed on the individual classifiers and then on the Ensemble classifiers. The ensemble classifier like Random Forest and ADABoost performed better with 100% Accuracy, Precision and Recall when compared to the individual classifiers with 99.16% accuracy, 98.8% Precision and 100% Recall obtained from Decision Tree Algorithm

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