State transition stochastic model for predictingofftooncooling schedule in dwellings as implemented using a multilayered artificial neural network

Abstract

Our previous study (Tanimoto, J. and Hagishima, A. 2005. State transition probability for the Markov model dealing with on/off cooling schedule in dwellings. Energy and Buildings, 37,[181][182][183][184][185][186][187] proposed a set of state transition probabilities for the Markov chain dealing with the on/off cooling schedule in dwellings. The probability of turning on an air conditioner was defined in the form of a sigmoid function by the indoor globe temperature. Obviously, a real stochastic event of shifting from the off to on state is affected by not only indoor thermal quality parameters but also by other complex factors, such as the presence of family members, time of the day and whether it is a weekday or holiday. In this article, we report an alternate model, based on a multilayered artificial neural network (MANN), for predicting the off to on cooling schedule. We gathered field measurement data on family dwellings during the summer of 2008 by deploying hygrothermometers with recording functions to measure the room temperature and the globe and blowout air temperature of the air conditioner. The MANN used has nine nodes in both its input and hidden layers and a single node in its output layer, which implies that the state is either shifting from off to on (1) or not (0). The information provided to the input layer nodes includes the time of the day, whether it is a weekday or holiday, the probability of the presence of inhabitants and the predicted percentage of dissatisfied (PPD) people. PPD, derived from PMV theory, is applied as a representative parameter of the indoor thermal quality, in place of the globe temperature, since it accounts for various influences. The field measurement datasets were divided into two parts: teaching data and data for validation. A model trained by the teaching data was confirmed to reproduce the state transition characteristic of the validation period, which seems complex and is determined by the behaviour of various inhabitants. The performance of the model in reproducing this behaviour is improved over that of the previous model derived from the Markov chain.

Total utility demand prediction for multi-dwelling sites by a bottom-up approach considering variations of inhabitants’ behaviour schedules

“…In the process, indoor anthropogenic heat gain derived from both electric appliances and human bodies is considered. Regarding how the HVAC turns on and off, a stochastic model with Markov chain (Tanimoto and Haghisma 2005) is applied to both heating and cooling, where two transition states, HVAC off-to-on and on-to-off, function depending on the indoor global temperature and outdoor air temperature to determine whether the next state is on or off, although we have a newly developed and more accurate stochastic model for cooling, which is based on multi-layer neural network (Tanimoto and Hagishima 2012). In this study, we assume a series of HP systems powered by electricity, where three sizes of capacities, 6-Jou, 8-Jou and 10-Jou classes, are available (a Jou is a unit of area in a Japanese Tatami room; 1 Jou ¼ 1.65 m 2 ).…”

Occupant behaviour in mixed-mode office buildings in a subtropical climate: Beyond typical models of adaptive actions

“…window and solar shading operations, adjusting thermostats, etc.). Several studies have been conducted on windows [16][17][18][19][20][21]39] and air-conditioning operation [22,23]. The majority of occupant behaviour studies come from North America, Europe and China [9].…”

Analysis of Occupants’ Air-Conditioning Opening Behaviour Based on the Survival Model

“…In the aspect of influencing factors, the factors affecting the action of air conditioning switch are divided into subjective factors and objective factors. Objective factors include environmental factors (temperature, humidity, wind speed) [5][6][7] and non-environmental factors (users' income, age) [8][9][10]. Subjective factors are the influence of personal factors (such as life style and preference) on air conditioning operation behavior [11,12]; In terms of model construction, scholars mainly explore the relationship between environmental factors and air conditioning switch probability, among which Logistic model [13] and Weibull model [14,15] are the mainstream models at present.…”