Forecasting hot water consumption in dwellings using artificial neural networks

The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to be used for shifting electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict individual hot water heater energy demand profile. Data from about a hundred dwellings are analysed using autocorrelation technique. The most appropriate lags were chosen and different Neural Network model topologies were tested and compared. The results are positive and show that water heaters have could potentially shift electric energy

Forecasting hot water consumption in residential houses

An increased number of intermittent renewables poses a threat to the system balance. As a result, new tools and concepts, like advanced demand-side management and smart grid technologies, are required for the demand to meet supply. There is a need for higher consumer awareness and automatic response to a shortage or surplus of electricity. The distributed water heater can be considered as one of the most energy-intensive devices, where its energy demand is shiftable in time without influencing the comfort level. Tailored hot water usage predictions and advanced control techniques could enable these devices to supply ancillary energy balancing services. The paper analyses a set of hot water consumption data from residential dwellings. This work is an important foundation for the development of a demand-side management strategy based on hot water consumption forecasting at the level of individual residential houses. Various forecasting models, such as exponential smoothing, seasonal autoregressive integrated moving average, seasonal decomposition and a combination of them, are fitted to test different prediction techniques. These models outperform the chosen benchmark models (mean, naive and seasonal naive) and show better performance measure values. The results suggest that seasonal decomposition of the time series plays the most significant part in the accuracy of forecasting

Managing renewable intermittency in smart grid:use of residential hot water heaters as a form of energy storage

This paper discusses a novel wind generation balancing technique to improve renewable energy integration to the system. Novel individual hot water heater controllers were modelled with the ability to forecast and look ahead the required energy, while responding to electricity grid imbalance. Artificial intelligence and machine learning techniques were used to learn and predict energy usage. In this research wind power data was used in most cases to represent the supply side, where focus was on the actual generation deviation from plan. It proved to be possible to balance the generation and increase system efficiency while maintaining user satisfaction. The methods developed in this research are not limited to wind power balancing and can also be used with any other type of renewable generation source

Distributed energy storage using residential hot water heaters

This paper proposes and analyses a new demand response technique for renewable energy regulation using smart hot water heaters that forecast water consumption at an individual dwelling level. Distributed thermal energy storage has many advantages, including high overall efficiency, use of existing infrastructure and a distributed nature. In addition, the use of a smart thermostatic controller enables the prediction of required water amounts and keeps temperatures at a level that minimises user discomfort while reacting to variations in the electricity network. Three cases are compared in this paper, normal operation, operation with demand response and operation following the proposed demand response mechanism that uses consumption forecasts. The results show that this technique can produce both up and down regulation, as well as increase water heater efficiency. When controlling water heaters without consumption forecast, the users experience discomfort in the form of hot water shortage, but after the full technique is applied, the shortage level drops to nearly the starting point. The amount of regulation power from a single dwelling is also discussed in this paper

Balancing of intermittent renewable generation in smart grid

This thesis researches a novel electricity demand response method and renewable energy management technique. It demonstrated the use of flow batteries and residential hot water heaters to balance wind power deviation from plan. The electricity supply-demand balancing problem becomes increasingly more difficult. A large portion of complexity to this problem comes from the fact that most renewable energy sources are inherently hard to control and intermittent. The increasing amount of renewable energy generation makes scientists research new supply-demand balancing possibilities to adapt for the changes. In this research wind power data was used in most cases to represent the supply side. The focus is on the actual generation deviation from plan, i.e. forecasting error. On the other hand, the methods developed in this thesis are not limited to wind power balancing. Two major approaches were analysed - heating ventilation and air conditioning system control (mainly focused on, but not limited to, residential hot water heaters) and hybrid power system comprising of thermal and hydro power plants together with utility scale flow batteries. These represent the consumption side or the demand response mechanism. The first approach focused on modelling the behaviour of residential end users. Artificial intelligence and machine learning techniques such as neural networks and Box-Jenkins methodology were used to learn and predict energy usage. Both joint and individual dwelling behaviour was considered. Model predictive control techniques were then used to send the exact real-time price and observe the change in electricity consumption. Also, novel individual hot water heater controllers were modelled with the ability to forecast and look ahead the required energy, while responding to electricity grid imbalance. It proved to be possible to balance the generation and increase system efficiency while maintaining user satisfaction. For the second approach, the hybrid multi-power plant system was exploited. Three different power sources were modelled, namely thermal power plant, hydro power pant and flow battery. These sources were ranked by the ability to rapidly change the output of electricity. The power that needs to be balanced was then routed to different power units according to their response times. The calculation of the best power dispatch is proposed using a cost function. The aim of this research was to accommodate for the wind power imbalance without sacrificing the health of the power plants (minimising load variations for sensitive units)

Hybrid wind power balance control strategy using thermal power, hydro power and flow batteries

The increased number of renewable power plants pose threat to power system balance. Their intermittent nature makes it very difficult to predict power output, thus either additional reserve power plants or new storage and control technologies are required. Traditional spinning reserve cannot fully compensate sudden changes in renewable energy power generation. Using new storage technologies such as flow batteries, it is feasible to balance the variations in power and voltage within very short period of time. This paper summarises the controlled use of hybrid flow battery, thermal and hydro power plant system, to support wind power plants to reach near perfect balance, i.e. make the total power output as close as possible to the predicted value. It also investigates the possibility of such technology to take part in the balance of the Lithuanian power system. A dynamic model of flow battery is demonstrated where it evaluates the main parameters such as power, energy, reaction time and efficiency. The required battery size is tested based on range of thermal and hydro power plant reaction times. This work suggests that power and energy of a reasonable size flow battery is sufficient to correct the load and wind power imbalance

Model Predictive Control for Building Active Demand Response Systems

The Active Demand Response (ADR), integrated with the distributed energy generation and storage systems, is the most common strategy for the optimization of energy consumption and indoor comfort in buildings, considering the energy availability and the balancing of the energy production from renewable sources. In the paper an overview of basic requirements and applications of ADR management is presented. Specifically, the model predictive control (MPC) adopted in several applications as optimal control strategy in the ADR buildings context is analysed. Finally the research experience of the authors in this context is described

Neural network based real-time pricing in demand side management for future smart grid

Electricity grid is currently being transformed into smart grid. Increased number of renewables require more and more ancillary services to backup intermittent power generation. A very important topic in tomorrow's electricity grid is demand side management. This tool should be used as an alternative for traditional backup power reserves. It requires a deep understanding on how consumption depends on dynamic pricing. This paper proposes a method for modelling the electricity demand response to a real-time pricing. A virtual smart house is modelled using Gridlab-D smart grid simulator. The HVAC system is setup to respond to real-time price sent by the utility. The paper is analysing the ability of neural network to predict the exact price, which is sent to the end user in order to maintain the supply balance in the system. It should also reduce the peaks in demand and increase system resilience

Demand side management in smart grid:a review and proposals for future direction

This paper mainly focuses on demand side management and demand response, including drivers and benefits, shiftable load scheduling methods and peak shaving techniques. Demand side management techniques found in literature are overviewed and a novel electricity demand control technique using real-time pricing is proposed. Currently users have no means to change their power consumption to benefit the whole system. The proposed method consists of modern system identification and control that would enable user side load control. This would potentially balance demand side with supply side more effectively and would also reduce peak demand and make the whole system more efficient