This research is related to the control of energy consumption and efficiency in building Heating Ventilation and Air Conditioning (HVAC) systems and is primarily concerned with controlling the function of heating. The main goal of this thesis is to develop a control system that can achieve the following two main control functions: a) detection of unexpected indoor conditions that may result in unnecessary power consumption and b) energy efficiency control regarding optimal balancing of two parameters: the required energy consumption for heating, versus thermal comfort of the occupants. Methods of both orientations were developed in a multi-zone space composed of nine zones where each zone is equipped with a wireless node consisting of temperature and occupancy sensors while all the scattered nodes together form a wireless sensor network (WSN). The main methods of both control functions utilize the potential of the deterministic subspace identification (SID) predictive model which provides the predicted temperature of the zones. In the main method for detecting unexpected situations that can directly affect the thermal condition of the indoor space and cause energy consumption (abnormal situations), the predictive temperature from the SID model is compared with the real temperature and thus possible temperature deviations that indicate unexpected situations are detected. The method successfully detects two situations: the high infiltration gain due to unexpected cold air intake from the external surroundings through potential unforeseen openings (windows, exterior doors, opened ceilings etc) as well as the high heat gain due to onset of fire. With the support of the statistical algorithm for abrupt change detection, Cumulative Sum (CUSUM), the detection of temperature deviations is accomplished with accuracy in a very short time. The CUSUM algorithm is first evaluated at an initial approach to detect power diversions due to the above situations caused by the aforementioned exogenous factors. The predicted temperature of the zone from the SID model utilized appropriately also by the main method of the second control function for energy efficiency control. The time needed for the temperature of a zone to reach the thermal comfort zone threshold from a low initial value is measured by the predicted temperature evolution, and this measurement bases the logic of a control criterion for applying proactive heating to the unoccupied zones or not. Additional key points for the control criterion of the method is the occupation time of the zones as well as the remaining time of the occupants in the occupied zones. Two scenarios are examined: the first scenario with two adjacent zones where the one is occupied and the other is not, and the second scenario with a multi-zone space where the occupants are moving through the zones in a cascade mode. Gama and Pareto probability distributions modeled the occupation times of the two-zone scenario while exponential distribution modeled the cascade scenario as the least favorable case. The mobility of the occupants modeled with a semi-Markov process and the method provides satisfactory and reasonable results. At an initial approach the proactive heating of the zones is evaluated with specific algorithms that handle appropriately the occupation time into the zones.