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The purpose of this study is to analyse the contribution of shifting the load of household devices from periods of renewable energy underproduction to surplus periods in order to better match energy production and demand.

An optimisation model for load shifting of household devices is developed and analysed with the help of a simulation of energy loads for the year 2030 in Germany.

About 1.48% of the renewable surplus energy can be used in addition which equals total savings between €15.06m and €106.71m. However, 24.47% of the surplus periods can be compensated completely and 6.38% up to a level of 94%.

A 100% diffusion of smart metres and devices and a high acceptance among consumers are assumed so that the results of this study present an upper bound for the contribution of household load shifting.

Load shifting of household devices provides only a small contribution and can only be one measure among others. It is crucial to find and invest in new energy storage systems so that more renewable spill-over energy can be used.

An optimisation model for load shifting is developed. Customer behaviour and devices are considered individually and not only a fixed percental share. Beside peak load periods also periods of renewable spill-over energy are considered.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Some of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa.

The research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior.

This study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions.

It should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households.

Load shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers.

This paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

Electricity utilization at electricity peak hour may differ from every single administration region, for example, mechanical region, business territory and residential zone. This paper introduces a demand-side load management (DSM) strategy, which is one of the utilization of smart grid (SG) that is fit for controlling loads inside the residential working so that the client fulfillment is augmented at least expense.

In this paper, a heuristic algorithms-based energy management controller is intended for a residential region in a SG. Here, Antlion Optimization technique is used for DSM techniques such as load shifting, peak clipping, and valley filling in the residential sectors for 24 h with the help of stochastic function to determine the detection of random distribution of the load.

This proposed algorithm offered the greatest fulfillment and least expense caused by the consumers when compared to the traditional cost by taking the individual consumer preferences for the loads and the ideal time scheduling for the load, which is obtained from the rebuilding trap.

Simulation results demonstrate that the comparison of the cost incurred by the users obtained by the DSM techniques is satisfiable.

Climate change reports from New Zealand claim that climate change will impact some cities such as Auckland from a heating-dominated to a cooling-dominated climate. The benefits and risks of climate change on buildings' thermal performance are still unknown. This paper examines the impacts of climate change on the energy performance of residential buildings in New Zealand and provides insight into changes in trends in energy consumption by quantifying the impacts of climate change.

The present paper used a downscaling method to generate weather data for three locations in New Zealand: Auckland, Wellington and Christchurch. The weather data sets were applied to the energy simulation of a residential case study as a reference building using a validated building energy analysis tool (EnergyPlus).

The result indicated that in Wellington and Christchurch, heating would be the major thermal load of residential buildings, while in Auckland, the main thermal load will change from heating to cooling in future years. The revised R-values for the building code will affect the pattern of dominant heating and cooling demands in buildings in Auckland in the future, while in Wellington and Christchurch, the heating load will be higher than the cooling load.

The findings of this study gave a broader insight into the risks and opportunities of climate change for the thermal performance of buildings. The results established the significance of considering climate change in energy performance analysis to inform the appropriate building codes for the design of residential buildings to avoid future costly changes to buildings.

The purpose of this paper is to investigate the impact of temperature on residential electricity demand in the city of Greater Accra, Ghana. It is believed that the increasing trend of temperatures may significantly affect people’s lives and demand for electricity from the national grid. Given the recurrent electricity crisis in Ghana, this study will investigate both the current and future residential energy demands in the light of temperature fluctuations. This will inform future power generation using renewable energy resources mix to find a sustainable solution to the recurrent energy demand challenges in Ghana. This study will help the Government of Ghana to better understand the temperature dependence of residential energy demand, which in turn will help in developing behavioral modification programs aimed at reducing energy consumption. Monthly data for the temperature and residential electricity consumption for Greater Accra Region from January 2007 to December 2018 obtained from the Ghana Meteorological Service (GMS) and Ghana Grid Company (Gridco), respectively, are used for the analysis.

This study used monthly time series data from 2007 to 2018. Data on monthly electricity demand and temperature are obtained from the Ghana Grid Company and GMS. The theoretical framework for residential electricity consumption, the log-linear demand equation and time series regression approaches was used for this study. To demonstrate certain desirable properties and to produce good estimators in this study, an analysis technique of ordinary least squares measurement was also applied.

This study showed an impact on residential electricity requirements in the selected regions of Greater Accra owing to temperature change. The analysis suggests a substantial positive response to an increase in temperature demand for residential electricity and thus indicates a growth of the region’s demand for electricity in the future because of temperature changes. As this analysis projects, the growth in the electricity demand seems too small for concern, perhaps because of the incoherence of the mechanisms used to regulate the temperature by the residents. However, two points should be considered when drawing any conclusions even in the case of Greater Accra alone. First, the growth in the demand for electricity shown in the present study is the growth of demand due only to increasing temperatures that do not consider changes in all the other factors driving the growth of demand. The electricity demand will in the future increase beyond what is induced by temperature, due to increasing demand, population and mechanization and other socioeconomic factors. Second, power consumption understated genuine electricity demand, owing to the massive shedding of loads (Dumsor) which occurred in Ghana from 2012 to 2015 in the analysis period that also applies in the Greater Accra region. Given both of these factors, the growth in demand for electricity is set to increase in response to climate change, which draws on the authorities to prepare more critically on capacity building which loads balancing. The results also revealed that monthly total residential electricity consumption, particularly the monthly peak electricity consumption in the city of Accra is highly sensitive to temperature. Therefore, the rise in temperature under different climate change scenarios would have a high impact on residential electricity consumption. This study reveals that the monthly total residential electricity demand in Greater Accra will increase by up to 3.1%.

The research data was largely restricted to only one region in Ghana because of the inconsistencies in the data from the other regions. The only climate variable use was temperature because it was proven in the literature that it was the most dominant variable that affects electricity demand, so it was not out of place to use only this variable. The research, however, can be extended to capture the entire regions of the country if sponsorship and accurate data can be obtained.

The government as the policy and law-making authority has to play the most influential role to ensure adaptation at all levels toward the impact of climate change for residential consumers. It is the main responsibility of the government to arrange enough supports to help residential consumers adapt to climate change and try to make consumers self-sufficient by modification of certain behaviors rather than supply dependent. Government bodies need to carefully define their climate adaptation supports and incentive programs to influence residential-level consumption practices and demand management. Here, energy policies and investments need to be more strategic. The most critical problem is to identify the appropriate adaptation policies that favor the most vulnerable sectors such as the residential sector.

To evaluate both mitigation and adaptation policies, it is important to estimate the effect of climate change on energy usage around the world. Existing empirical figures, however, are concentrated in Western nations, especially the USA. To predict how electricity usage will shift in the city of Greater Accra, Ghana, the authors used regular household electricity consumption data.

The motivation for this paper and in particular the empirical analysis for Ghana is originality for the literature. This paper demonstrates an adequate understanding of the relevant literature in modern times.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.